{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-07T06:45:44.541446Z",
     "iopub.status.busy": "2023-11-07T06:45:44.541127Z",
     "iopub.status.idle": "2023-11-07T06:45:44.545907Z",
     "shell.execute_reply": "2023-11-07T06:45:44.545286Z",
     "shell.execute_reply.started": "2023-11-07T06:45:44.541412Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from tqdm import tqdm\n",
    "import os\n",
    "import time\n",
    "import gc\n",
    "from sklearn.model_selection import StratifiedKFold, KFold\n",
    "from sklearn.metrics import roc_auc_score,f1_score\n",
    "from sklearn.decomposition import TruncatedSVD\n",
    "from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer\n",
    "from scipy import sparse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-07T06:45:44.684622Z",
     "iopub.status.busy": "2023-11-07T06:45:44.684429Z",
     "iopub.status.idle": "2023-11-07T06:45:44.691308Z",
     "shell.execute_reply": "2023-11-07T06:45:44.690809Z",
     "shell.execute_reply.started": "2023-11-07T06:45:44.684597Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#定义描述性函数 \n",
    "import seaborn as sns\n",
    "def stat_df(df):\n",
    "    stats = []\n",
    "    for col in df.columns:\n",
    "        stats.append((col, df[col].nunique(),df[col].isnull().sum()*100/df.shape[0],\n",
    "                     df[col].value_counts(normalize=True,dropna=False).values[0]*100,df[col].dtype))\n",
    "    stats_df = pd.DataFrame(stats, columns=['特征','唯一数数量','缺失值占比','最多数占比','类型'])\n",
    "    stats_df.sort_values('缺失值占比',ascending=False)\n",
    "    return stats_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1 读取特征"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-07T06:45:44.807410Z",
     "iopub.status.busy": "2023-11-07T06:45:44.807225Z",
     "iopub.status.idle": "2023-11-07T06:45:44.859247Z",
     "shell.execute_reply": "2023-11-07T06:45:44.858805Z",
     "shell.execute_reply.started": "2023-11-07T06:45:44.807388Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(41287, 196)"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#1 表1 B_1_trnflw.pkl\n",
    "import pickle\n",
    "file_name1=\"./feature/B_1_trnflw.pkl\"\n",
    "with open(file_name1, 'rb') as file:\n",
    "    trnflw_feature = pickle.load(file)\n",
    "trnflw_feature.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-07T06:45:44.953020Z",
     "iopub.status.busy": "2023-11-07T06:45:44.952840Z",
     "iopub.status.idle": "2023-11-07T06:45:44.991125Z",
     "shell.execute_reply": "2023-11-07T06:45:44.990684Z",
     "shell.execute_reply.started": "2023-11-07T06:45:44.952998Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(41945, 123)"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#2 表2 B_2_QRYTRNFLW.pkl\n",
    "file_name1=\"./feature/B_2_QRYTRNFLW.pkl\"\n",
    "with open(file_name1, 'rb') as file:\n",
    "    QRYTRNFLW_feature = pickle.load(file)\n",
    "QRYTRNFLW_feature.shape\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-07T06:45:45.105647Z",
     "iopub.status.busy": "2023-11-07T06:45:45.105457Z",
     "iopub.status.idle": "2023-11-07T06:45:45.114826Z",
     "shell.execute_reply": "2023-11-07T06:45:45.114382Z",
     "shell.execute_reply.started": "2023-11-07T06:45:45.105626Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(490, 196)"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#3 表3 B_3_CSTLOG.pkl\n",
    "file_name1=\"./feature/B_3_CSTLOG.pkl\"\n",
    "with open(file_name1, 'rb') as file:\n",
    "    CSTLOG_feature = pickle.load(file)\n",
    "CSTLOG_feature.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-07T06:45:45.222070Z",
     "iopub.status.busy": "2023-11-07T06:45:45.221864Z",
     "iopub.status.idle": "2023-11-07T06:45:45.231092Z",
     "shell.execute_reply": "2023-11-07T06:45:45.230647Z",
     "shell.execute_reply.started": "2023-11-07T06:45:45.222047Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1082, 123)"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#4 表4 B_4_CSTLOGQUERY.pkl\n",
    "file_name1=\"./feature/B_4_CSTLOGQUERY.pkl\"\n",
    "with open(file_name1, 'rb') as file:\n",
    "    CSTLOGQUERY_feature = pickle.load(file)\n",
    "CSTLOGQUERY_feature.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-07T06:45:45.329216Z",
     "iopub.status.busy": "2023-11-07T06:45:45.329017Z",
     "iopub.status.idle": "2023-11-07T06:45:45.387591Z",
     "shell.execute_reply": "2023-11-07T06:45:45.387148Z",
     "shell.execute_reply.started": "2023-11-07T06:45:45.329194Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(42016, 267)"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#5 表5 B_5_APS.pkl\n",
    "file_name1=\"./feature/B_5_APS.pkl\"\n",
    "with open(file_name1, 'rb') as file:\n",
    "    APS_feature = pickle.load(file)\n",
    "APS_feature.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-07T06:45:45.461791Z",
     "iopub.status.busy": "2023-11-07T06:45:45.461567Z",
     "iopub.status.idle": "2023-11-07T06:45:45.487995Z",
     "shell.execute_reply": "2023-11-07T06:45:45.487556Z",
     "shell.execute_reply.started": "2023-11-07T06:45:45.461757Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(42139, 51)"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#6 表6&&7 B_6&&7_asset.pkl\n",
    "file_name1=\"./feature/B_6&&7_asset.pkl\"\n",
    "with open(file_name1, 'rb') as file:\n",
    "    asset_feature = pickle.load(file)\n",
    "asset_feature.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-07T06:45:45.583768Z",
     "iopub.status.busy": "2023-11-07T06:45:45.583591Z",
     "iopub.status.idle": "2023-11-07T06:45:45.600672Z",
     "shell.execute_reply": "2023-11-07T06:45:45.600239Z",
     "shell.execute_reply.started": "2023-11-07T06:45:45.583747Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(42139, 16)"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#7 表8 LYH_8_feature_PROD_feature_v1.pkl\n",
    "file_name1=\"./feature/B_LYH_8_feature_PROD_feature_v1.pkl\"\n",
    "with open(file_name1, 'rb') as file:\n",
    "    PROD_feature = pickle.load(file)\n",
    "PROD_feature.shape\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-07T06:45:45.680365Z",
     "iopub.status.busy": "2023-11-07T06:45:45.680177Z",
     "iopub.status.idle": "2023-11-07T06:45:45.695629Z",
     "shell.execute_reply": "2023-11-07T06:45:45.695189Z",
     "shell.execute_reply.started": "2023-11-07T06:45:45.680343Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(42139, 9)"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#8 表9 B_9_nature.pkl\n",
    "file_name1=\"./feature/B_9_nature.pkl\"\n",
    "with open(file_name1, 'rb') as file:\n",
    "    nature_feature = pickle.load(file)\n",
    "nature_feature.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-07T06:45:45.822246Z",
     "iopub.status.busy": "2023-11-07T06:45:45.822056Z",
     "iopub.status.idle": "2023-11-07T06:45:45.845955Z",
     "shell.execute_reply": "2023-11-07T06:45:45.845521Z",
     "shell.execute_reply.started": "2023-11-07T06:45:45.822223Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(42139, 62)"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#9 LYH_业务特征 LYH_4_work_feature_v2\n",
    "file_name1=\"./feature/B_LYH_4_work_feature_v2.pkl\"\n",
    "with open(file_name1, 'rb') as file:\n",
    "    work_feature = pickle.load(file)\n",
    "work_feature.shape\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-07T06:45:45.960324Z",
     "iopub.status.busy": "2023-11-07T06:45:45.960132Z",
     "iopub.status.idle": "2023-11-07T06:45:46.441638Z",
     "shell.execute_reply": "2023-11-07T06:45:46.441159Z",
     "shell.execute_reply.started": "2023-11-07T06:45:45.960302Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(37050, 1784)"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#10-11 俊杰特征\n",
    "file_name1=\"./feature/B_hjj_train.pkl\"\n",
    "with open(file_name1, 'rb') as file:\n",
    "    hjj_train_feature = pickle.load(file)\n",
    "hjj_train_feature.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-07T06:45:46.442742Z",
     "iopub.status.busy": "2023-11-07T06:45:46.442561Z",
     "iopub.status.idle": "2023-11-07T06:45:46.480432Z",
     "shell.execute_reply": "2023-11-07T06:45:46.479983Z",
     "shell.execute_reply.started": "2023-11-07T06:45:46.442720Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5089, 1784)"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "file_name1=\"./feature/B_hjj_test.pkl\"\n",
    "with open(file_name1, 'rb') as file:\n",
    "    hjj_test_feature = pickle.load(file)\n",
    "hjj_test_feature.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-07T06:45:46.481618Z",
     "iopub.status.busy": "2023-11-07T06:45:46.481444Z",
     "iopub.status.idle": "2023-11-07T06:45:48.002022Z",
     "shell.execute_reply": "2023-11-07T06:45:48.001514Z",
     "shell.execute_reply.started": "2023-11-07T06:45:46.481596Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(42139, 1784)"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hjj_feature=pd.concat([hjj_train_feature, hjj_test_feature], axis = 0).reset_index(drop = True)\n",
    "hjj_feature.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-07T06:45:48.003303Z",
     "iopub.status.busy": "2023-11-07T06:45:48.003106Z",
     "iopub.status.idle": "2023-11-07T06:45:48.052063Z",
     "shell.execute_reply": "2023-11-07T06:45:48.051579Z",
     "shell.execute_reply.started": "2023-11-07T06:45:48.003279Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(42139, 213)"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#12 4个表的补充特征\n",
    "file_name1=\"./feature/B_1234_hebing.pkl\"\n",
    "with open(file_name1, 'rb') as file:\n",
    "    yxh_1234_hebing = pickle.load(file)\n",
    "yxh_1234_hebing.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-07T06:45:48.053079Z",
     "iopub.status.busy": "2023-11-07T06:45:48.052891Z",
     "iopub.status.idle": "2023-11-07T06:45:48.069732Z",
     "shell.execute_reply": "2023-11-07T06:45:48.069281Z",
     "shell.execute_reply.started": "2023-11-07T06:45:48.053057Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(42139, 9)"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#13 LYH_LR_feature\n",
    "file_name1=\"./feature/B_LYH_LR_feature.pkl\"\n",
    "with open(file_name1, 'rb') as file:\n",
    "    LYH_LR_feature = pickle.load(file)\n",
    "LYH_LR_feature.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-07T06:45:48.070679Z",
     "iopub.status.busy": "2023-11-07T06:45:48.070505Z",
     "iopub.status.idle": "2023-11-07T06:45:48.095456Z",
     "shell.execute_reply": "2023-11-07T06:45:48.095005Z",
     "shell.execute_reply.started": "2023-11-07T06:45:48.070658Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(41945, 66)"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#14 LYH_2_QRYTRNFLW_feature_v1\n",
    "file_name1=\"./feature/B_LYH_2_QRYTRNFLW_feature_v1.pkl\"\n",
    "with open(file_name1, 'rb') as file:\n",
    "    LYH_2_feature = pickle.load(file)\n",
    "LYH_2_feature.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-07T06:45:48.097052Z",
     "iopub.status.busy": "2023-11-07T06:45:48.096854Z",
     "iopub.status.idle": "2023-11-07T06:45:48.102493Z",
     "shell.execute_reply": "2023-11-07T06:45:48.102050Z",
     "shell.execute_reply.started": "2023-11-07T06:45:48.097023Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1082, 66)"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#15 LYH_4_CSTLOGQUERY_feature_v1\n",
    "file_name1=\"./feature/B_LYH_4_CSTLOGQUERY_feature_v1.pkl\"\n",
    "with open(file_name1, 'rb') as file:\n",
    "    LYH_4_feature = pickle.load(file)\n",
    "LYH_4_feature.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-07T06:45:48.103677Z",
     "iopub.status.busy": "2023-11-07T06:45:48.103509Z",
     "iopub.status.idle": "2023-11-07T06:45:48.631400Z",
     "shell.execute_reply": "2023-11-07T06:45:48.630916Z",
     "shell.execute_reply.started": "2023-11-07T06:45:48.103656Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(42139, 1709)"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#16 加业务特征\n",
    "file_name1=\"./feature/B_other_1029.pkl\"\n",
    "with open(file_name1, 'rb') as file:\n",
    "    other_group = pickle.load(file)\n",
    "other_group.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2 合并所有特征"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-07T06:45:48.632498Z",
     "iopub.status.busy": "2023-11-07T06:45:48.632300Z",
     "iopub.status.idle": "2023-11-07T06:46:03.647022Z",
     "shell.execute_reply": "2023-11-07T06:46:03.646516Z",
     "shell.execute_reply.started": "2023-11-07T06:45:48.632475Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(42139, 4879)"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "TARGET_train = pd.read_csv('../contest/train/TARGET_QZ.csv')   #目标客户表\n",
    "TARGET_B = pd.read_csv('../contest/B/TARGET_QZ_B.csv')   #目标客户表\n",
    "target_data=pd.concat([TARGET_train, TARGET_B], axis = 0).reset_index(drop = True)\n",
    "\n",
    "data=pd.DataFrame()\n",
    "\n",
    "data=target_data.merge(trnflw_feature, on = 'CUST_NO', how = 'left') #1\n",
    "\n",
    "data = data.merge(QRYTRNFLW_feature, on = 'CUST_NO', how = 'left') #2\n",
    "\n",
    "data = data.merge(CSTLOG_feature, on = 'CUST_NO', how = 'left') #3\n",
    "\n",
    "data = data.merge(CSTLOGQUERY_feature, on = 'CUST_NO', how = 'left') #4 \n",
    "\n",
    "data = data.merge(APS_feature, on = 'CUST_NO', how = 'left') #5 \n",
    "\n",
    "data= data.merge(asset_feature, on = 'CUST_NO', how = 'left') #6\n",
    "\n",
    "data = data.merge(PROD_feature, on = 'CUST_NO', how = 'left')#7\n",
    "\n",
    "data = data.merge(nature_feature, on = 'CUST_NO', how = 'left') #8\n",
    "\n",
    "data = data.merge(work_feature, on = 'CUST_NO', how = 'left') #9\n",
    "\n",
    "data = data.merge(hjj_feature, on = 'CUST_NO', how = 'left') #10-11\n",
    "\n",
    "data = data.merge(yxh_1234_hebing, on = 'CUST_NO', how = 'left') #12\n",
    "\n",
    "data = data.merge(LYH_LR_feature, on = 'CUST_NO', how = 'left') #13\n",
    "\n",
    "data = data.merge(LYH_2_feature, on = 'CUST_NO', how = 'left') #14\n",
    "\n",
    "data = data.merge(LYH_4_feature, on = 'CUST_NO', how = 'left') #15\n",
    "\n",
    "data = data.merge(other_group, on = 'CUST_NO', how = 'left') #16\n",
    "\n",
    "\n",
    "data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3 建模前预处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-07T06:46:03.648176Z",
     "iopub.status.busy": "2023-11-07T06:46:03.647965Z",
     "iopub.status.idle": "2023-11-07T06:46:26.253333Z",
     "shell.execute_reply": "2023-11-07T06:46:26.252837Z",
     "shell.execute_reply.started": "2023-11-07T06:46:03.648151Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(763,)"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res = stat_df(data)\n",
    "res = res[res['缺失值占比'] > 80]\n",
    "missing = np.array(res['特征'])\n",
    "np.shape(missing)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-07T06:46:26.254437Z",
     "iopub.status.busy": "2023-11-07T06:46:26.254249Z",
     "iopub.status.idle": "2023-11-07T06:46:37.977391Z",
     "shell.execute_reply": "2023-11-07T06:46:37.976716Z",
     "shell.execute_reply.started": "2023-11-07T06:46:26.254413Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "data_test = data[data[\"FLAG\"].isnull() == True].copy().reset_index(drop=True)\n",
    "data_train = data[~data[\"FLAG\"].isnull() == True].copy().reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-07T06:46:37.978620Z",
     "iopub.status.busy": "2023-11-07T06:46:37.978419Z",
     "iopub.status.idle": "2023-11-07T06:46:37.982488Z",
     "shell.execute_reply": "2023-11-07T06:46:37.981829Z",
     "shell.execute_reply.started": "2023-11-07T06:46:37.978595Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def statis_feat(df_know, df_unknow,cat_list):\n",
    "    for f in tqdm(cat_list):\n",
    "        df_unknow = stat(df_know, df_unknow, [f], {'FLAG': ['mean']})\n",
    "    return df_unknow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-07T06:46:37.983474Z",
     "iopub.status.busy": "2023-11-07T06:46:37.983295Z",
     "iopub.status.idle": "2023-11-07T06:46:37.988776Z",
     "shell.execute_reply": "2023-11-07T06:46:37.988251Z",
     "shell.execute_reply.started": "2023-11-07T06:46:37.983452Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "cat_list = ['NTRL_CUST_SEX_CD','NTRL_RANK_CD_A','NTRL_RANK_CD_B','NTRL_RANK_CD_C','NTRL_RANK_CD_D','NTRL_RANK_CD_E','NTRL_RANK_CD_F']\n",
    "def stat(df, df_merge, group_by, agg):\n",
    "    group = df.groupby(group_by).agg(agg)\n",
    "    columns = []\n",
    "    for on, methods in agg.items():\n",
    "        for method in methods:\n",
    "            columns.append('{}_{}_{}'.format('_'.join(group_by), on, method))\n",
    "    group.columns = columns\n",
    "    group.reset_index(inplace=True)\n",
    "    df_merge = df_merge.merge(group, on=group_by, how='left')\n",
    "    del (group)\n",
    "    gc.collect()\n",
    "    return df_merge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-07T06:46:37.989708Z",
     "iopub.status.busy": "2023-11-07T06:46:37.989530Z",
     "iopub.status.idle": "2023-11-07T06:47:21.131618Z",
     "shell.execute_reply": "2023-11-07T06:47:21.130975Z",
     "shell.execute_reply.started": "2023-11-07T06:46:37.989688Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 7/7 [00:03<00:00,  2.06it/s]\n",
      "100%|██████████| 7/7 [00:03<00:00,  2.06it/s]\n",
      "100%|██████████| 7/7 [00:03<00:00,  2.08it/s]\n",
      "100%|██████████| 7/7 [00:03<00:00,  2.07it/s]\n",
      "100%|██████████| 7/7 [00:03<00:00,  2.06it/s]\n"
     ]
    }
   ],
   "source": [
    "df_stas_feat = None\n",
    "kf = StratifiedKFold(n_splits=5, random_state=2020, shuffle=True)\n",
    "for train_index, val_index in kf.split(data_train, data_train['FLAG']):\n",
    "    df_fold_train = data_train.iloc[train_index]\n",
    "    df_fold_val = data_train.iloc[val_index]\n",
    "    df_fold_val = statis_feat(df_fold_train, df_fold_val,cat_list)\n",
    "    df_stas_feat = pd.concat([df_stas_feat, df_fold_val], axis=0)\n",
    "    del (df_fold_train)\n",
    "    del (df_fold_val)\n",
    "    gc.collect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-07T06:47:21.132827Z",
     "iopub.status.busy": "2023-11-07T06:47:21.132635Z",
     "iopub.status.idle": "2023-11-07T06:47:31.632259Z",
     "shell.execute_reply": "2023-11-07T06:47:31.631566Z",
     "shell.execute_reply.started": "2023-11-07T06:47:21.132805Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 7/7 [00:01<00:00,  3.75it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(80,)\n"
     ]
    }
   ],
   "source": [
    "#生成df_test/df_train\n",
    "df_test = statis_feat(data_train, data_test, cat_list)\n",
    "df_train=pd.DataFrame(df_stas_feat)\n",
    "\n",
    "# 预删除特征 \n",
    "drop_cols = ['CUST_NO','FLAG','DATA_DAT','DATA_DAT_x','CARD_NO']\n",
    "\n",
    "# 要删除的缺失占比太大的特征\n",
    "for fea in missing:  \n",
    "    drop_cols.append(fea)\n",
    "np.shape(drop_cols)\n",
    "\n",
    "#删除唯一值为1的特征\n",
    "unique_1_cols = []\n",
    "for col in [col for col in data.columns ]:\n",
    "    #print(col)\n",
    "    if data[col].nunique() < 2:\n",
    "        unique_1_cols.append(col)\n",
    "print(np.shape(unique_1_cols))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-07T06:47:31.633384Z",
     "iopub.status.busy": "2023-11-07T06:47:31.633184Z",
     "iopub.status.idle": "2023-11-07T06:47:39.048442Z",
     "shell.execute_reply": "2023-11-07T06:47:39.047711Z",
     "shell.execute_reply.started": "2023-11-07T06:47:31.633358Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "feature_name = [i for i in df_train.columns if i not in drop_cols+unique_1_cols] \n",
    "#null_imp后用\n",
    "#feature_name=used_feat1\n",
    "X_train = df_train[feature_name].reset_index(drop=True)\n",
    "X_test = df_test[feature_name].reset_index(drop=True)\n",
    "y = df_train['FLAG'].reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-07T06:47:39.049545Z",
     "iopub.status.busy": "2023-11-07T06:47:39.049347Z",
     "iopub.status.idle": "2023-11-07T06:47:39.053408Z",
     "shell.execute_reply": "2023-11-07T06:47:39.052911Z",
     "shell.execute_reply.started": "2023-11-07T06:47:39.049520Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5089, 4053)"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape\n",
    "X_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4 先做1轮 Null_importance 特征筛选"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-07T06:47:39.054493Z",
     "iopub.status.busy": "2023-11-07T06:47:39.054311Z",
     "iopub.status.idle": "2023-11-07T06:47:39.060523Z",
     "shell.execute_reply": "2023-11-07T06:47:39.060027Z",
     "shell.execute_reply.started": "2023-11-07T06:47:39.054471Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\n# from lightgbm import LGBMClassifier\\nimport lightgbm as lgb\\nimport matplotlib.pyplot as plt\\ndef xuanze(used_feat,ycol,mode,seed):\\n    seed = 2021\\n\\n    print(\\'build lgb\\') \\n#     model = LGBMClassifier(objective=\\'binary\\',boosting_type=\\'gbdt\\',num_leaves=63,max_depth=-1,learning_rate=0.01,n_estimators=600,subsample=0.8,\\n#                           feature_fraction=0.8,random_state=seed,is_unbalance=True,metric= [\\'auc\\',\\'binary_logloss\\'],n_jobs=20,bagging_freq=5)\\n    \\n    \\n   # model = XGBClassifier(boosting_type=\\'gbdt\\',num_leaves=15,max_depth=6,learning_rate=0.01,n_estimators=1200,subsample=0.8,tree_method = \\'gpu_hist\\',\\n   #                        feature_fraction=0.7,random_state=seed,is_unbalance=True,eval_metric=\\'auc\\')\\n    \\n    #model = CatBoostClassifier(iterations=1400, depth=8, learning_rate=0.01, loss_function=\\'Logloss\\',\\n    #                             #cat_features= [\\'x625\\', \\'x626\\'], \\n    #                             verbose=True, eval_metric=\\'AUC\\', counter_calc_method=\\'Full\\',task_type=\\'GPU\\',use_best_model=True,\\n    #                             metric_period=100)\\n    \\n    \\n#     X_train = df_train[feature_name].reset_index(drop=True)\\n#     X_test = df_test[feature_name].reset_index(drop=True)\\n#     y = df_train[\\'FLAG\\'].reset_index(drop=True)\\n    \\n    X_train = df_train[used_feat]\\n    if mode == True:\\n        Y_train = df_train[ycol]\\n    else:\\n        Y_train = df_train[ycol].copy().sample(frac=1.0,random_state= seed)\\n\\n    print(\\'lgb is processing\\')\\n    dtrain = lgb.Dataset(X_train, label=Y_train)\\n#     dval = lgb.Dataset(test_x, label=test_y)\\n    parameters = {\\n        \\'learning_rate\\': 0.01,  \\n        \\'max_depth\\': -1,#-1\\n        \\'boosting_type\\': \\'gbdt\\',\\n        \\'objective\\': \\'binary\\',\\n        \\'metric\\': [\\'auc\\',\\'binary_logloss\\',\\'binary_error\\'],\\n        \\'num_leaves\\': 12, #\\'num_leaves\\': 15, \\n        \\'feature_fraction\\': 0.8,\\n        \\'bagging_fraction\\': 0.8,\\n        \\'bagging_freq\\': 5,\\n        \\'seed\\': 2021,\\n        # \\'lambda_l2\\': 1,\\n        \\'bagging_seed\\': 1,\\n        \\'feature_fraction_seed\\': 7,\\n        \\'min_data_in_leaf\\': 20,  #\\n        \\'verbose\\': -1, \\n        \\'n_jobs\\':8\\n    }\\n    lgb_model = lgb.train(\\n            parameters,\\n            dtrain,\\n            num_boost_round=1000,\\n            valid_sets=[dtrain],\\n#             valid_names=[\"train\",\"valid\"],\\n            early_stopping_rounds=800, #early_stopping_rounds=200,early_stopping_rounds=500，early_stopping_rounds=150，#165\\n            verbose_eval=100,\\n\\n        )\\n    \\n    \\n#     lgb_model = model.fit(X_train,\\n#                           Y_train,\\n#                           #eval_names=[\\'train\\', \\'valid\\'],\\n#                           eval_set=[(X_train, Y_train)],\\n#                           verbose=100,\\n#                           #categorical_feature = [\\'catory\\'],\\n#                           early_stopping_rounds=200\\n#                           )\\n\\n\\n    df_importance = pd.DataFrame({\\n        \\'column\\': used_feat,\\n        \\'importance\\': lgb_model.feature_importance(),\\n    })\\n    \\n    print(\\'lgb complete\\')\\n    return df_importance\\n\\nused_feat = feature_name\\nycol = \\'FLAG\\'\\n\\nimp1 = xuanze(used_feat,ycol,True,209)\\nimp2 = xuanze(used_feat,ycol,False,4096)\\nimp3 = xuanze(used_feat,ycol,False,2048)\\nimp4 = xuanze(used_feat,ycol,False,1024)\\nimp5 = xuanze(used_feat,ycol,False,2015)\\nimp6 = xuanze(used_feat,ycol,False,1015)\\nimp7 = xuanze(used_feat,ycol,False,820)\\nimp8 = xuanze(used_feat,ycol,False,55)\\nimp9 = xuanze(used_feat,ycol,False,66)\\nimp10 = xuanze(used_feat,ycol,False,90)\\nimp11 = xuanze(used_feat,ycol,False,100)\\n\\nimp1.columns =  [\\'column\\',\\'importance_1\\']\\nimp2.columns =  [\\'column\\',\\'importance_2\\']\\nimp3.columns =  [\\'column\\',\\'importance_3\\']\\nimp4.columns =  [\\'column\\',\\'importance_4\\']\\nimp5.columns =  [\\'column\\',\\'importance_5\\']\\nimp6.columns =  [\\'column\\',\\'importance_6\\']\\nimp7.columns =  [\\'column\\',\\'importance_7\\']\\nimp8.columns =  [\\'column\\',\\'importance_8\\']\\nimp9.columns =  [\\'column\\',\\'importance_9\\']\\nimp10.columns =  [\\'column\\',\\'importance_10\\']\\nimp11.columns =  [\\'column\\',\\'importance_11\\']\\n\\nimp = pd.merge(imp1,imp2,on = [\\'column\\'],how = \\'left\\')\\nimp = pd.merge(imp,imp3,on = [\\'column\\'],how = \\'left\\')\\nimp = pd.merge(imp,imp4,on = [\\'column\\'],how = \\'left\\')\\nimp = pd.merge(imp,imp5,on = [\\'column\\'],how = \\'left\\')\\nimp = pd.merge(imp,imp6,on = [\\'column\\'],how = \\'left\\')\\nimp = pd.merge(imp,imp7,on = [\\'column\\'],how = \\'left\\')\\nimp = pd.merge(imp,imp8,on = [\\'column\\'],how = \\'left\\')\\nimp = pd.merge(imp,imp9,on = [\\'column\\'],how = \\'left\\')\\nimp = pd.merge(imp,imp10,on = [\\'column\\'],how = \\'left\\')\\nimp = pd.merge(imp,imp11,on = [\\'column\\'],how = \\'left\\')\\nfea = [\\'importance_{}\\'.format(i) for i in range(2,12)]\\nimp[\\'mean\\'] = imp[fea].mean(axis = 1)\\nused_feat1 = imp.loc[imp[\\'importance_1\\']>imp[\\'mean\\']][\\'column\\'].values\\nused_feat1 = list(used_feat1)\\n'"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''\n",
    "# from lightgbm import LGBMClassifier\n",
    "import lightgbm as lgb\n",
    "import matplotlib.pyplot as plt\n",
    "def xuanze(used_feat,ycol,mode,seed):\n",
    "    seed = 2021\n",
    "\n",
    "    print('build lgb') \n",
    "#     model = LGBMClassifier(objective='binary',boosting_type='gbdt',num_leaves=63,max_depth=-1,learning_rate=0.01,n_estimators=600,subsample=0.8,\n",
    "#                           feature_fraction=0.8,random_state=seed,is_unbalance=True,metric= ['auc','binary_logloss'],n_jobs=20,bagging_freq=5)\n",
    "    \n",
    "    \n",
    "   # model = XGBClassifier(boosting_type='gbdt',num_leaves=15,max_depth=6,learning_rate=0.01,n_estimators=1200,subsample=0.8,tree_method = 'gpu_hist',\n",
    "   #                        feature_fraction=0.7,random_state=seed,is_unbalance=True,eval_metric='auc')\n",
    "    \n",
    "    #model = CatBoostClassifier(iterations=1400, depth=8, learning_rate=0.01, loss_function='Logloss',\n",
    "    #                             #cat_features= ['x625', 'x626'], \n",
    "    #                             verbose=True, eval_metric='AUC', counter_calc_method='Full',task_type='GPU',use_best_model=True,\n",
    "    #                             metric_period=100)\n",
    "    \n",
    "    \n",
    "#     X_train = df_train[feature_name].reset_index(drop=True)\n",
    "#     X_test = df_test[feature_name].reset_index(drop=True)\n",
    "#     y = df_train['FLAG'].reset_index(drop=True)\n",
    "    \n",
    "    X_train = df_train[used_feat]\n",
    "    if mode == True:\n",
    "        Y_train = df_train[ycol]\n",
    "    else:\n",
    "        Y_train = df_train[ycol].copy().sample(frac=1.0,random_state= seed)\n",
    "\n",
    "    print('lgb is processing')\n",
    "    dtrain = lgb.Dataset(X_train, label=Y_train)\n",
    "#     dval = lgb.Dataset(test_x, label=test_y)\n",
    "    parameters = {\n",
    "        'learning_rate': 0.01,  \n",
    "        'max_depth': -1,#-1\n",
    "        'boosting_type': 'gbdt',\n",
    "        'objective': 'binary',\n",
    "        'metric': ['auc','binary_logloss','binary_error'],\n",
    "        'num_leaves': 12, #'num_leaves': 15, \n",
    "        'feature_fraction': 0.8,\n",
    "        'bagging_fraction': 0.8,\n",
    "        'bagging_freq': 5,\n",
    "        'seed': 2021,\n",
    "        # 'lambda_l2': 1,\n",
    "        'bagging_seed': 1,\n",
    "        'feature_fraction_seed': 7,\n",
    "        'min_data_in_leaf': 20,  #\n",
    "        'verbose': -1, \n",
    "        'n_jobs':8\n",
    "    }\n",
    "    lgb_model = lgb.train(\n",
    "            parameters,\n",
    "            dtrain,\n",
    "            num_boost_round=1000,\n",
    "            valid_sets=[dtrain],\n",
    "#             valid_names=[\"train\",\"valid\"],\n",
    "            early_stopping_rounds=800, #early_stopping_rounds=200,early_stopping_rounds=500，early_stopping_rounds=150，#165\n",
    "            verbose_eval=100,\n",
    "\n",
    "        )\n",
    "    \n",
    "    \n",
    "#     lgb_model = model.fit(X_train,\n",
    "#                           Y_train,\n",
    "#                           #eval_names=['train', 'valid'],\n",
    "#                           eval_set=[(X_train, Y_train)],\n",
    "#                           verbose=100,\n",
    "#                           #categorical_feature = ['catory'],\n",
    "#                           early_stopping_rounds=200\n",
    "#                           )\n",
    "\n",
    "\n",
    "    df_importance = pd.DataFrame({\n",
    "        'column': used_feat,\n",
    "        'importance': lgb_model.feature_importance(),\n",
    "    })\n",
    "    \n",
    "    print('lgb complete')\n",
    "    return df_importance\n",
    "\n",
    "used_feat = feature_name\n",
    "ycol = 'FLAG'\n",
    "\n",
    "imp1 = xuanze(used_feat,ycol,True,209)\n",
    "imp2 = xuanze(used_feat,ycol,False,4096)\n",
    "imp3 = xuanze(used_feat,ycol,False,2048)\n",
    "imp4 = xuanze(used_feat,ycol,False,1024)\n",
    "imp5 = xuanze(used_feat,ycol,False,2015)\n",
    "imp6 = xuanze(used_feat,ycol,False,1015)\n",
    "imp7 = xuanze(used_feat,ycol,False,820)\n",
    "imp8 = xuanze(used_feat,ycol,False,55)\n",
    "imp9 = xuanze(used_feat,ycol,False,66)\n",
    "imp10 = xuanze(used_feat,ycol,False,90)\n",
    "imp11 = xuanze(used_feat,ycol,False,100)\n",
    "\n",
    "imp1.columns =  ['column','importance_1']\n",
    "imp2.columns =  ['column','importance_2']\n",
    "imp3.columns =  ['column','importance_3']\n",
    "imp4.columns =  ['column','importance_4']\n",
    "imp5.columns =  ['column','importance_5']\n",
    "imp6.columns =  ['column','importance_6']\n",
    "imp7.columns =  ['column','importance_7']\n",
    "imp8.columns =  ['column','importance_8']\n",
    "imp9.columns =  ['column','importance_9']\n",
    "imp10.columns =  ['column','importance_10']\n",
    "imp11.columns =  ['column','importance_11']\n",
    "\n",
    "imp = pd.merge(imp1,imp2,on = ['column'],how = 'left')\n",
    "imp = pd.merge(imp,imp3,on = ['column'],how = 'left')\n",
    "imp = pd.merge(imp,imp4,on = ['column'],how = 'left')\n",
    "imp = pd.merge(imp,imp5,on = ['column'],how = 'left')\n",
    "imp = pd.merge(imp,imp6,on = ['column'],how = 'left')\n",
    "imp = pd.merge(imp,imp7,on = ['column'],how = 'left')\n",
    "imp = pd.merge(imp,imp8,on = ['column'],how = 'left')\n",
    "imp = pd.merge(imp,imp9,on = ['column'],how = 'left')\n",
    "imp = pd.merge(imp,imp10,on = ['column'],how = 'left')\n",
    "imp = pd.merge(imp,imp11,on = ['column'],how = 'left')\n",
    "fea = ['importance_{}'.format(i) for i in range(2,12)]\n",
    "imp['mean'] = imp[fea].mean(axis = 1)\n",
    "used_feat1 = imp.loc[imp['importance_1']>imp['mean']]['column'].values\n",
    "used_feat1 = list(used_feat1)\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-07T06:47:39.061406Z",
     "iopub.status.busy": "2023-11-07T06:47:39.061239Z",
     "iopub.status.idle": "2023-11-07T06:47:39.063677Z",
     "shell.execute_reply": "2023-11-07T06:47:39.063190Z",
     "shell.execute_reply.started": "2023-11-07T06:47:39.061385Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#np.shape(used_feat1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5 模型训练"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-07T06:47:39.064948Z",
     "iopub.status.busy": "2023-11-07T06:47:39.064764Z",
     "iopub.status.idle": "2023-11-07T06:47:39.119416Z",
     "shell.execute_reply": "2023-11-07T06:47:39.118886Z",
     "shell.execute_reply.started": "2023-11-07T06:47:39.064927Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "feature_name=['trnflw_custno_times_sum', 'trnflw_custno_times_min', 'trnflw_custno_times_std', 'trnflw_custno_time_diff_mean', 'trnflw_custno_time_diff_sum', 'trnflw_custno_time_diff_median', 'trnflw_custno_time_diff_max', 'trnflw_custno_time_diff_min', 'trnflw_custno_time_diff_skew', 'trnflw_custno_time_diff_std', 'trnflw_custno_time_diff_kurt', 'trnflw_mon_TFT_DTE_month_sum', 'trnflw_mon_TFT_DTE_month_std', 'trnflw_mon_montimes_max', 'trnflw_mon_montimes_min', 'trnflw_mon_montimes_std', 'trnflw_day_TFT_DTE_day_sum', 'trnflw_day_TFT_DTE_day_max', 'trnflw_day_TFT_DTE_day_kurt', 'trnflw_day_daytimes_mean', 'trnflw_day_daytimes_std', 'trnflw_cnoAMT_TFT_TRNAMT_mean', 'trnflw_cnoAMT_TFT_TRNAMT_sum', 'trnflw_cnoAMT_TFT_TRNAMT_max', 'trnflw_cnoAMT_TFT_TRNAMT_min', 'trnflw_cnoAMT_TFT_TRNAMT_std', 'trnflw_mAMT_TFT_DTE_month_sum', 'trnflw_mAMT_TFT_DTE_month_std', 'trnflw_mAMT_TFT_TRNAMT_sum', 'trnflw_mAMT_TFT_TRNAMT_max', 'trnflw_mAMT_TFT_TRNAMT_min', 'trnflw_mAMT_TFT_TRNAMT_std', 'trnflw_dAMT_TFT_DTE_day_mean', 'trnflw_dAMT_TFT_DTE_day_sum', 'trnflw_dAMT_TFT_DTE_day_max', 'trnflw_dAMT_TFT_DTE_day_std', 'trnflw_dAMT_TFT_TRNAMT_sum', 'trnflw_dAMT_TFT_TRNAMT_max', 'trnflw_dAMT_TFT_TRNAMT_std', 'trnflw_dcod_TFT_DTE_day_nunique', 'times_std_trnflw_last14', 'times_kurt_trnflw_last14', 'time_diff_mean_trnflw_last14', 'time_diff_median_trnflw_last14', 'time_diff_skew_trnflw_last14', 'time_diff_std_trnflw_last14', 'times_mean_trnflw_last28', 'times_sum_trnflw_last28', 'times_std_trnflw_last28', 'time_diff_mean_trnflw_last28', 'time_diff_sum_trnflw_last28', 'time_diff_median_trnflw_last28', 'time_diff_kurt_trnflw_last28', 'times_mean_trnflw_last42', 'times_sum_trnflw_last42', 'time_diff_sum_trnflw_last42', 'time_diff_median_trnflw_last42', 'time_diff_std_trnflw_last56', 'time_diff_trnflw_rate_last42', 'TFT_TRNAMT_mean_trnflwam_last14', 'TFT_TRNAMT_sum_trnflwam_last28', 'TFT_TRNAMT_max_trnflwam_last28', 'TFT_TRNAMT_min_trnflwam_last28', 'TFT_TRNAMT_max_trnflwam_last42', 'TFT_TRNAMT_min_trnflwam_last42', 'TFT_TRNAMT_max_trnflwam_last56', 'TFT_TRNAMT_dr_mean_last28', 'TFT_TRNAMT_dr_mean_last42', 'TFT_TRNAMT_dr_max_last42', 'TFT_TRNAMT_dr_min_last42', 'TFT_TRNAMT_dr_min_last56', 'QRYTRNFLW_times_mean', 'QRYTRNFLW_times_sum', 'QRYTRNFLW_times_max', 'QRYTRNFLW_times_std', 'QRYTRNFLW_time_diff_median', 'QRYTRNFLW_time_diff_min', 'QRYTRNFLW_m_TFT_DTE_month_sum', 'QRYTRNFLW_m_montimes_mean', 'QRYTRNFLW_m_montimes_max', 'QRYTRNFLW_m_montimes_skew', 'QRYTRNFLW_d_daytimes_mean', 'QRYTRNFLW_d_daytimes_median', 'QRYTRNFLW_d_daytimes_max', 'QRYTRNFLW_d_daytimes_std', 'QRYTRNFLW_cod_TFT_STDBSNCOD_nunique', 'QRYTRNFLW_mcod_TFT_STDBSNCOD_nunique', 'QRYTRNFLW_dcod_TFT_STDBSNCOD_nunique', 'time_diff_sum_QRYTRNFLW_last14', 'time_diff_median_QRYTRNFLW_last14', 'time_diff_skew_QRYTRNFLW_last14', 'times_mean_QRYTRNFLW_last28', 'times_sum_QRYTRNFLW_last28', 'times_max_QRYTRNFLW_last28', 'times_skew_QRYTRNFLW_last28', 'times_std_QRYTRNFLW_last28', 'time_diff_mean_QRYTRNFLW_last28', 'time_diff_max_QRYTRNFLW_last28', 'times_std_QRYTRNFLW_last42', 'times_std_QRYTRNFLW_last56', 'time_diff_mean_QRYTRNFLW_last56', 'times_QRYTRNFLW_rate_last14', 'time_QRYTRNFLW_diff_rate_last14', 'time_QRYTRNFLW_diff_rate_last42', 'APS_times_mean', 'APS_times_sum', 'APS_times_std', 'APS_time_diff_mean', 'APS_time_diff_sum', 'APS_time_diff_median', 'APS_time_diff_min', 'APS_m_APSDTRDAT_TM_month_sum', 'APS_m_montimes_skew', 'APS_d_APSDTRDAT_TM_day_min', 'APS_d_APSDTRDAT_TM_day_std', 'APS_d_daytimes_std', 'APS_outamt_APSDTRAMT_std', 'APS_moutamt_APSDTRDAT_TM_month_mean', 'APS_moutamt_APSDTRAMT_std', 'APS_moutamt_APSDTRAMT_kurt', 'APS_doutamt_APSDTRDAT_TM_day_sum', 'APS_doutamt_APSDTRAMT_mean', 'APS_doutamt_APSDTRAMT_max', 'APS_inamt_APSDTRAMT_mean', 'APS_inamt_APSDTRAMT_max', 'APS_inamt_APSDTRAMT_min', 'APS_inmamt_APSDTRDAT_TM_month_skew', 'APS_inmamt_APSDTRAMT_mean', 'APS_inmamt_APSDTRAMT_skew', 'APS_indamt_APSDTRDAT_TM_day_sum', 'APS_indamt_APSDTRAMT_mean', 'APS_macc_APSDCPTPRDNO_nunique', 'times_mean_APS_last14', 'times_median_APS_last14', 'times_max_APS_last14', 'time_diff_mean_APS_last14', 'time_diff_skew_APS_last14', 'time_diff_std_APS_last14', 'times_mean_APS_last28', 'times_std_APS_last28', 'time_diff_sum_APS_last28', 'times_mean_APS_last42', 'times_kurt_APS_last42', 'time_diff_std_APS_last42', 'times_mean_APS_last56', 'times_APS_rate_last28', 'times_APS_rate_last56', 'APSDTRAMT_max_APSout_last28', 'APSDTRAMT_max_APSout_last56', 'APSDTRAMT_mean_APSin_last14', 'APSDTRAMT_sum_APSin_last14', 'APSDTRAMT_max_APSin_last14', 'APSDTRAMT_mean_APSin_last28', 'APSDTRAMT_min_APSin_last28', 'APSDTRAMT_mean_APSin_last42', 'in_APSDTRAMT_dr_mean_last28', 'in_APSDTRAMT_dr_mean_last42', 'DAY_FA_BAL', 'MAVER_FA_BAL', 'SAVER_FA_BAL', 'YAVER_FA_BAL', 'DAY_AUM_BAL', 'MAVER_AUM_BAL', 'SAVER_AUM_BAL', 'YAVER_AUM_BAL', 'DPSA_BAL', 'MAVER_DPSA_BAL', 'SAVER_DPSA_BAL', 'YAVER_DPSA_BAL', 'aum_y', 'DPSA_s', 'SAVER_dingqi_aum', 'TDPT_PAY_IND', 'NTRL_CUST_AGE', 'NTRL_CUST_SEX_CD', 'NTRL_RANK_CD_B', 'APSDTRDAT_TM_H7', 'APSDTRAMT_in_sum_7', 'APSDTRAMT_out_sum_7', 'APSDTRAMT_count_rate_7', 'APSDTRAMT_sum_rate_7', 'APSDTRDAT_TM_H14', 'APSDTRAMT_in_sum_14', 'APSDTRAMT_out_sum_14', 'APSDTRAMT_in_count_21', 'APSDTRDAT_TM_H28', 'APSDTRAMT_in_sum_28', 'APSDTRAMT_out_sum_28', 'APSDTRAMT_count_rate_28', 'APSDTRAMT_in_count_35', 'APSDTRAMT_out_count_35', 'APSDCPTPRDNO_night_amt_out', 'aps_6_same_amt_timesmean', 'stranger_num4', 'stranger_num7', 'stranger_num13', 'APS_QZ_small_test', 'APSDTRDAT_TM_date_count40', 'APSDCPTPRDNO_tfidf_0', 'APSDCPTPRDNO_tfidf_1', 'APSDCPTPRDNO_tfidf_2', 'APSDCPTPRDNO_tfidf_3', 'APSDCPTPRDNO_tfidf_4', 'APSDCPTPRDNO_tfidf_5', 'APSDCPTPRDNO_tfidf_6', 'APSDCPTPRDNO_tfidf_7', 'APSDCPTPRDNO_tfidf_9', 'APSDCPTPRDNO_countvec_2', 'APSDCPTPRDNO_countvec_3', 'APSDCPTPRDNO_countvec_5', 'APSDCPTPRDNO_countvec_8', 'APSDTRCOD_tfidf_1', 'APSDTRCOD_tfidf_2', 'APSDTRCOD_tfidf_3', 'APSDTRCOD_tfidf_5', 'APSDTRCOD_countvec_1', 'APSDTRCOD_countvec_3', 'APSDTRCOD_countvec_7', 'APSDTRCOD_countvec_9', 'APSDTRCHL_tfidf_0', 'APSDTRCHL_tfidf_3', 'APSDTRCHL_tfidf_5', 'APSDTRCHL_countvec_8', 'CARD_NO_CARD_NO_APSDCPTPRDNO_w2v_3', 'CARD_NO_CARD_NO_APSDCPTPRDNO_w2v_4', 'CARD_NO_APSDCPTPRDNO_w2v_1', 'CARD_NO_APSDCPTPRDNO_w2v_4', 'CARD_NO_APSDCPTPRDNO_w2v_5', 'CARD_NO_APSDTRCOD_w2v_0', 'CARD_NO_APSDTRCOD_w2v_1', 'CARD_NO_APSDTRCOD_w2v_4', 'CARD_NO_APSDTRCOD_w2v_6', 'CARD_NO_APSDTRCHL_w2v_1', 'CARD_NO_APSDTRCHL_w2v_2', 'CARD_NO_APSDTRCHL_w2v_6', 'APSDTRAMT_ptp_aps_qz', 'APSDTRAMT_ABS_max_aps_qz', 'APSDTRAMT_ABS_min_aps_qz', 'APSDTRAMT_ABS_median_aps_qz', 'APSDTRAMT_ABS_std_aps_qz', 'APSDTRAMT_ABS_sum_aps_qz', 'month_mean_aps_qz', 'day_mean_aps_qz', 'hour_median_aps_qz', 'hour_std_aps_qz', 'hour_skew_aps_qz', 'minute_skew_aps_qz', 'minute_sum_aps_qz', 'dayofweek_median_aps_qz', 'dayofweek_skew_aps_qz', 'daygap_std_aps_qz', 'daygap_skew_aps_qz', 'daygap_sum_aps_qz', 'APSDTRAMT_mean_aps_qz_01fc06d5103d851bc6fa02dd1f1a3fe5', 'APSDTRAMT_mean_aps_qz_3c6c4e45e982dc80246b40db4bab70c3', 'APSDTRAMT_mean_aps_qz_3cc8c0101d1d765139bd9b79d2ba64b0', 'APSDTRAMT_mean_aps_qz_88851f9e0e9b482eacf54d9c91d3e01f', 'APSDTRAMT_max_aps_qz_50a111f66d3a4bf0b67fd5e1453a99e8', 'APSDTRAMT_min_aps_qz_3c6c4e45e982dc80246b40db4bab70c3', 'APSDTRAMT_min_aps_qz_3cc8c0101d1d765139bd9b79d2ba64b0', 'APSDTRAMT_min_aps_qz_88851f9e0e9b482eacf54d9c91d3e01f', 'APSDTRAMT_median_aps_qz_01fc06d5103d851bc6fa02dd1f1a3fe5', 'APSDTRAMT_median_aps_qz_1488b47add3c27be40af16a79e78f465', 'APSDTRAMT_median_aps_qz_3c6c4e45e982dc80246b40db4bab70c3', 'APSDTRAMT_median_aps_qz_3cc8c0101d1d765139bd9b79d2ba64b0', 'APSDTRAMT_median_aps_qz_88851f9e0e9b482eacf54d9c91d3e01f', 'APSDTRAMT_std_aps_qz_1488b47add3c27be40af16a79e78f465', 'APSDTRAMT_std_aps_qz_3c6c4e45e982dc80246b40db4bab70c3', 'APSDTRAMT_std_aps_qz_3cc8c0101d1d765139bd9b79d2ba64b0', 'APSDTRAMT_ptp_aps_qz_1488b47add3c27be40af16a79e78f465', 'APSDTRAMT_ptp_aps_qz_c29de004ad06129c243b586ca8dea80f', 'APSDTRAMT_sum_aps_qz_01fc06d5103d851bc6fa02dd1f1a3fe5', 'APSDTRAMT_sum_aps_qz_3cc8c0101d1d765139bd9b79d2ba64b0', 'APSDTRAMT_sum_aps_qz_88851f9e0e9b482eacf54d9c91d3e01f', 'APSDTRAMT_ABS_mean_aps_qz_01fc06d5103d851bc6fa02dd1f1a3fe5', 'APSDTRAMT_ABS_mean_aps_qz_88851f9e0e9b482eacf54d9c91d3e01f', 'APSDTRAMT_ABS_max_aps_qz_01fc06d5103d851bc6fa02dd1f1a3fe5', 'APSDTRAMT_ABS_max_aps_qz_50a111f66d3a4bf0b67fd5e1453a99e8', 'APSDTRAMT_ABS_min_aps_qz_88851f9e0e9b482eacf54d9c91d3e01f', 'APSDTRAMT_ABS_median_aps_qz_01fc06d5103d851bc6fa02dd1f1a3fe5', 'APSDTRAMT_ABS_median_aps_qz_50a111f66d3a4bf0b67fd5e1453a99e8', 'APSDTRAMT_ABS_median_aps_qz_88851f9e0e9b482eacf54d9c91d3e01f', 'APSDTRAMT_ABS_std_aps_qz_01fc06d5103d851bc6fa02dd1f1a3fe5', 'APSDTRAMT_ABS_std_aps_qz_88851f9e0e9b482eacf54d9c91d3e01f', 'APSDTRAMT_ABS_skew_aps_qz_01fc06d5103d851bc6fa02dd1f1a3fe5', 'month_mean_aps_qz_3cc8c0101d1d765139bd9b79d2ba64b0', 'month_mean_aps_qz_50a111f66d3a4bf0b67fd5e1453a99e8', 'month_max_aps_qz_3cc8c0101d1d765139bd9b79d2ba64b0', 'month_std_aps_qz_3cc8c0101d1d765139bd9b79d2ba64b0', 'month_skew_aps_qz_3cc8c0101d1d765139bd9b79d2ba64b0', 'month_sum_aps_qz_1488b47add3c27be40af16a79e78f465', 'month_sum_aps_qz_27eecfaa93503c43a3adc94d5eada0cd', 'month_sum_aps_qz_3c6c4e45e982dc80246b40db4bab70c3', 'month_sum_aps_qz_88851f9e0e9b482eacf54d9c91d3e01f', 'day_mean_aps_qz_1488b47add3c27be40af16a79e78f465', 'day_mean_aps_qz_27eecfaa93503c43a3adc94d5eada0cd', 'day_mean_aps_qz_3c6c4e45e982dc80246b40db4bab70c3', 'day_max_aps_qz_01fc06d5103d851bc6fa02dd1f1a3fe5', 'day_max_aps_qz_1488b47add3c27be40af16a79e78f465', 'day_min_aps_qz_27eecfaa93503c43a3adc94d5eada0cd', 'day_std_aps_qz_50a111f66d3a4bf0b67fd5e1453a99e8', 'day_skew_aps_qz_88851f9e0e9b482eacf54d9c91d3e01f', 'day_ptp_aps_qz_1488b47add3c27be40af16a79e78f465', 'day_ptp_aps_qz_c29de004ad06129c243b586ca8dea80f', 'day_sum_aps_qz_3cc8c0101d1d765139bd9b79d2ba64b0', 'hour_mean_aps_qz_3c6c4e45e982dc80246b40db4bab70c3', 'hour_mean_aps_qz_3cc8c0101d1d765139bd9b79d2ba64b0', 'hour_mean_aps_qz_50a111f66d3a4bf0b67fd5e1453a99e8', 'hour_max_aps_qz_1488b47add3c27be40af16a79e78f465','hour_min_aps_qz_01fc06d5103d851bc6fa02dd1f1a3fe5', 'hour_min_aps_qz_3c6c4e45e982dc80246b40db4bab70c3', 'hour_min_aps_qz_c29de004ad06129c243b586ca8dea80f', 'hour_median_aps_qz_01fc06d5103d851bc6fa02dd1f1a3fe5', 'hour_median_aps_qz_1488b47add3c27be40af16a79e78f465', 'hour_median_aps_qz_3c6c4e45e982dc80246b40db4bab70c3', 'hour_median_aps_qz_c29de004ad06129c243b586ca8dea80f', 'hour_std_aps_qz_1488b47add3c27be40af16a79e78f465', 'hour_std_aps_qz_3c6c4e45e982dc80246b40db4bab70c3', 'hour_std_aps_qz_3cc8c0101d1d765139bd9b79d2ba64b0', 'hour_skew_aps_qz_3cc8c0101d1d765139bd9b79d2ba64b0', 'hour_ptp_aps_qz_3c6c4e45e982dc80246b40db4bab70c3', 'hour_ptp_aps_qz_c29de004ad06129c243b586ca8dea80f', 'hour_sum_aps_qz_88851f9e0e9b482eacf54d9c91d3e01f', 'hour_sum_aps_qz_c29de004ad06129c243b586ca8dea80f', 'minute_mean_aps_qz_01fc06d5103d851bc6fa02dd1f1a3fe5', 'minute_mean_aps_qz_27eecfaa93503c43a3adc94d5eada0cd', 'minute_max_aps_qz_3c6c4e45e982dc80246b40db4bab70c3', 'minute_max_aps_qz_c29de004ad06129c243b586ca8dea80f', 'minute_min_aps_qz_1488b47add3c27be40af16a79e78f465', 'minute_median_aps_qz_27eecfaa93503c43a3adc94d5eada0cd', 'minute_std_aps_qz_01fc06d5103d851bc6fa02dd1f1a3fe5', 'minute_std_aps_qz_3c6c4e45e982dc80246b40db4bab70c3', 'minute_ptp_aps_qz_01fc06d5103d851bc6fa02dd1f1a3fe5', 'minute_ptp_aps_qz_c29de004ad06129c243b586ca8dea80f', 'minute_sum_aps_qz_3cc8c0101d1d765139bd9b79d2ba64b0', 'minute_sum_aps_qz_c29de004ad06129c243b586ca8dea80f', 'dayofweek_mean_aps_qz_3cc8c0101d1d765139bd9b79d2ba64b0', 'dayofweek_mean_aps_qz_88851f9e0e9b482eacf54d9c91d3e01f', 'dayofweek_max_aps_qz_50a111f66d3a4bf0b67fd5e1453a99e8', 'dayofweek_min_aps_qz_27eecfaa93503c43a3adc94d5eada0cd', 'dayofweek_min_aps_qz_3cc8c0101d1d765139bd9b79d2ba64b0', 'dayofweek_min_aps_qz_88851f9e0e9b482eacf54d9c91d3e01f', 'dayofweek_min_aps_qz_c29de004ad06129c243b586ca8dea80f', 'dayofweek_std_aps_qz_01fc06d5103d851bc6fa02dd1f1a3fe5', 'dayofweek_skew_aps_qz_3c6c4e45e982dc80246b40db4bab70c3', 'dayofweek_ptp_aps_qz_01fc06d5103d851bc6fa02dd1f1a3fe5', 'dayofweek_ptp_aps_qz_3cc8c0101d1d765139bd9b79d2ba64b0', 'dayofweek_sum_aps_qz_3cc8c0101d1d765139bd9b79d2ba64b0', 'daygap_mean_aps_qz_01fc06d5103d851bc6fa02dd1f1a3fe5', 'daygap_mean_aps_qz_27eecfaa93503c43a3adc94d5eada0cd', 'daygap_mean_aps_qz_3c6c4e45e982dc80246b40db4bab70c3', 'daygap_mean_aps_qz_88851f9e0e9b482eacf54d9c91d3e01f', 'daygap_mean_aps_qz_c29de004ad06129c243b586ca8dea80f', 'daygap_max_aps_qz_1488b47add3c27be40af16a79e78f465', 'daygap_max_aps_qz_27eecfaa93503c43a3adc94d5eada0cd', 'daygap_min_aps_qz_27eecfaa93503c43a3adc94d5eada0cd', 'daygap_min_aps_qz_88851f9e0e9b482eacf54d9c91d3e01f', 'daygap_median_aps_qz_01fc06d5103d851bc6fa02dd1f1a3fe5', 'daygap_median_aps_qz_1488b47add3c27be40af16a79e78f465', 'daygap_median_aps_qz_27eecfaa93503c43a3adc94d5eada0cd', 'daygap_std_aps_qz_3cc8c0101d1d765139bd9b79d2ba64b0', 'daygap_std_aps_qz_88851f9e0e9b482eacf54d9c91d3e01f', 'daygap_skew_aps_qz_3cc8c0101d1d765139bd9b79d2ba64b0', 'daygap_ptp_aps_qz_01fc06d5103d851bc6fa02dd1f1a3fe5', 'daygap_ptp_aps_qz_1488b47add3c27be40af16a79e78f465', 'daygap_ptp_aps_qz_3cc8c0101d1d765139bd9b79d2ba64b0', 'daygap_ptp_aps_qz_88851f9e0e9b482eacf54d9c91d3e01f', 'daygap_ptp_aps_qz_c29de004ad06129c243b586ca8dea80f', 'daygap_sum_aps_qz_27eecfaa93503c43a3adc94d5eada0cd', 'daygap_sum_aps_qz_3cc8c0101d1d765139bd9b79d2ba64b0', 'daygap_sum_aps_qz_50a111f66d3a4bf0b67fd5e1453a99e8', 'APSDTRAMT_mean_aps_qz_0e30a5a85d29c720619212ed0721f096', 'APSDTRAMT_mean_aps_qz_1d1fe13c960cff7c6f9dedeeb23a7524', 'APSDTRAMT_mean_aps_qz_6d9345571f7032b7281da7f93d1ffb22', 'APSDTRAMT_max_aps_qz_1d1fe13c960cff7c6f9dedeeb23a7524', 'APSDTRAMT_min_aps_qz_1d1fe13c960cff7c6f9dedeeb23a7524', 'APSDTRAMT_min_aps_qz_8c9c388064b85b10604063ea2f015e7b', 'APSDTRAMT_median_aps_qz_0e30a5a85d29c720619212ed0721f096', 'APSDTRAMT_median_aps_qz_6d9345571f7032b7281da7f93d1ffb22', 'APSDTRAMT_std_aps_qz_1d1fe13c960cff7c6f9dedeeb23a7524', 'APSDTRAMT_ptp_aps_qz_0e30a5a85d29c720619212ed0721f096', 'APSDTRAMT_ptp_aps_qz_6d9345571f7032b7281da7f93d1ffb22', 'APSDTRAMT_ptp_aps_qz_8c9c388064b85b10604063ea2f015e7b', 'APSDTRAMT_sum_aps_qz_6d9345571f7032b7281da7f93d1ffb22', 'APSDTRAMT_ABS_mean_aps_qz_c203106fbee9e0c716712d29be26b9eb', 'APSDTRAMT_ABS_max_aps_qz_0e30a5a85d29c720619212ed0721f096', 'APSDTRAMT_ABS_min_aps_qz_c203106fbee9e0c716712d29be26b9eb', 'APSDTRAMT_ABS_std_aps_qz_c203106fbee9e0c716712d29be26b9eb', 'APSDTRAMT_ABS_skew_aps_qz_c203106fbee9e0c716712d29be26b9eb', 'APSDTRAMT_ABS_ptp_aps_qz_0e30a5a85d29c720619212ed0721f096', 'APSDTRAMT_ABS_ptp_aps_qz_8c9c388064b85b10604063ea2f015e7b', 'APSDTRAMT_ABS_sum_aps_qz_6d9345571f7032b7281da7f93d1ffb22', 'month_mean_aps_qz_0e30a5a85d29c720619212ed0721f096', 'month_mean_aps_qz_6d9345571f7032b7281da7f93d1ffb22', 'month_mean_aps_qz_c203106fbee9e0c716712d29be26b9eb', 'month_skew_aps_qz_1d1fe13c960cff7c6f9dedeeb23a7524', 'month_skew_aps_qz_c203106fbee9e0c716712d29be26b9eb', 'month_sum_aps_qz_6d9345571f7032b7281da7f93d1ffb22', 'day_skew_aps_qz_c203106fbee9e0c716712d29be26b9eb', 'day_sum_aps_qz_6d9345571f7032b7281da7f93d1ffb22', 'day_sum_aps_qz_c203106fbee9e0c716712d29be26b9eb', 'hour_min_aps_qz_1d1fe13c960cff7c6f9dedeeb23a7524', 'hour_median_aps_qz_1d1fe13c960cff7c6f9dedeeb23a7524', 'hour_std_aps_qz_0e30a5a85d29c720619212ed0721f096', 'hour_skew_aps_qz_c203106fbee9e0c716712d29be26b9eb', 'hour_ptp_aps_qz_1d1fe13c960cff7c6f9dedeeb23a7524', 'hour_ptp_aps_qz_c203106fbee9e0c716712d29be26b9eb', 'minute_mean_aps_qz_1d1fe13c960cff7c6f9dedeeb23a7524', 'minute_min_aps_qz_1d1fe13c960cff7c6f9dedeeb23a7524', 'minute_min_aps_qz_c203106fbee9e0c716712d29be26b9eb', 'minute_std_aps_qz_1d1fe13c960cff7c6f9dedeeb23a7524', 'minute_ptp_aps_qz_8c9c388064b85b10604063ea2f015e7b', 'minute_sum_aps_qz_8c9c388064b85b10604063ea2f015e7b', 'dayofweek_mean_aps_qz_0e30a5a85d29c720619212ed0721f096', 'dayofweek_mean_aps_qz_1d1fe13c960cff7c6f9dedeeb23a7524', 'dayofweek_mean_aps_qz_c203106fbee9e0c716712d29be26b9eb', 'dayofweek_median_aps_qz_0e30a5a85d29c720619212ed0721f096', 'dayofweek_std_aps_qz_c203106fbee9e0c716712d29be26b9eb', 'dayofweek_skew_aps_qz_1d1fe13c960cff7c6f9dedeeb23a7524', 'dayofweek_skew_aps_qz_c203106fbee9e0c716712d29be26b9eb', 'dayofweek_ptp_aps_qz_c203106fbee9e0c716712d29be26b9eb', 'dayofweek_sum_aps_qz_1d1fe13c960cff7c6f9dedeeb23a7524', 'dayofweek_sum_aps_qz_6d9345571f7032b7281da7f93d1ffb22', 'daygap_mean_aps_qz_1d1fe13c960cff7c6f9dedeeb23a7524', 'daygap_mean_aps_qz_8c9c388064b85b10604063ea2f015e7b', 'daygap_mean_aps_qz_c203106fbee9e0c716712d29be26b9eb', 'daygap_max_aps_qz_0e30a5a85d29c720619212ed0721f096', 'daygap_max_aps_qz_8c9c388064b85b10604063ea2f015e7b', 'daygap_max_aps_qz_c203106fbee9e0c716712d29be26b9eb', 'daygap_min_aps_qz_0e30a5a85d29c720619212ed0721f096', 'daygap_min_aps_qz_1d1fe13c960cff7c6f9dedeeb23a7524', 'daygap_min_aps_qz_8c9c388064b85b10604063ea2f015e7b', 'daygap_min_aps_qz_c203106fbee9e0c716712d29be26b9eb', 'daygap_median_aps_qz_0e30a5a85d29c720619212ed0721f096', 'daygap_median_aps_qz_1d1fe13c960cff7c6f9dedeeb23a7524', 'daygap_median_aps_qz_c203106fbee9e0c716712d29be26b9eb', 'daygap_std_aps_qz_1d1fe13c960cff7c6f9dedeeb23a7524', 'daygap_ptp_aps_qz_1d1fe13c960cff7c6f9dedeeb23a7524', 'daygap_sum_aps_qz_1d1fe13c960cff7c6f9dedeeb23a7524', 'daygap_sum_aps_qz_6d9345571f7032b7281da7f93d1ffb22', 'APSDCPTPRDNO_nunique_aps_qz_01fc06d5103d851bc6fa02dd1f1a3fe5', 'APSDCPTPRDNO_nunique_aps_qz_3c6c4e45e982dc80246b40db4bab70c3', 'APSDCPTPRDNO_nunique_aps_qz_88851f9e0e9b482eacf54d9c91d3e01f', 'APSDCPTPRDNO_nunique_aps_qz_0e30a5a85d29c720619212ed0721f096', 'APSDCPTPRDNO_nunique_aps_qz_8c9c388064b85b10604063ea2f015e7b', 'APSDTRCOD_per_01fc06d5103d851bc6fa02dd1f1a3fe5', 'APSDTRCOD_per_3c6c4e45e982dc80246b40db4bab70c3', 'APSDTRCOD_per_3cc8c0101d1d765139bd9b79d2ba64b0', 'APSDTRCOD_per_c29de004ad06129c243b586ca8dea80f', 'APSDTRCHL_per_8c9c388064b85b10604063ea2f015e7b', 'aps_time_diff_min', 'aps_time_diff_median', 'CARD_NO_count_day_hour_max', 'CARD_NO_count_day_hour_std', 'CARD_NO_count_day_hour_mean_3', 'CARD_NO_count_day_hour_std_2', 'CARD_NO_count_day_hour_std_3', 'CARD_NO_count_day_hour_sum_3', 'APSDTRAMT_sum_day_APSDTRAMT_in_max', 'APSDTRAMT_sum_day_APSDTRAMT_in_skew', 'APSDTRAMT_sum_day_APSDTRAMT_in_sum', 'APSDTRAMT_sum_day_APSDTRAMT_out_max', 'APSDTRAMT_sum_day_APSDTRAMT_out_min', 'APSDTRAMT_sum_day_APSDTRAMT_out_std', 'APSDTRAMT_out/in_min', 'APSDTRAMT_sum_day_APSDTRAMT_in_mean_1', 'APSDTRAMT_sum_day_APSDTRAMT_in_median_3', 'APSDTRAMT_sum_day_APSDTRAMT_in_std_3', 'APSDTRAMT_sum_day_APSDTRAMT_in_sum_1', 'APSDTRAMT_sum_day_APSDTRAMT_out_max_1', 'APSDTRAMT_sum_day_APSDTRAMT_out_max_2', 'APSDTRAMT_sum_day_APSDTRAMT_out_median_1', 'APSDTRAMT_sum_day_APSDTRAMT_out_std_1', 'APSDTRAMT_sum_day_APSDTRAMT_out_std_3', 'APSDTRAMT_sum_day_APSDTRAMT_out_sum_1', 'APSDTRAMT_sum_day_APSDTRAMT_min_1', 'APSDTRAMT_sum_day_APSDTRAMT_median_3', 'APSDTRAMT_sum_day_APSDTRAMT_std_1', 'APSDTRAMT_out/in_min_3', 'APSDTRAMT_out/in_std_2', 'APSDTRAMT_out/in_sum_3', 'TFT_TRNAMT_median_mbank_trn', 'TFT_TRNAMT_ptp_mbank_trn', 'month_std_mbank_trn', 'month_skew_mbank_trn', 'day_median_mbank_trn', 'day_std_mbank_trn', 'day_ptp_mbank_trn', 'day_sum_mbank_trn', 'hour_mean_mbank_trn', 'hour_min_mbank_trn', 'hour_median_mbank_trn', 'minute_std_mbank_trn', 'dayofweek_mean_mbank_trn', 'dayofweek_std_mbank_trn', 'dayofweek_ptp_mbank_trn', 'daygap_min_mbank_trn', 'daygap_median_mbank_trn', 'daygap_std_mbank_trn', 'daygap_ptp_mbank_trn', 'TFT_STDBSNCOD_tfidf_1_x', 'TFT_STDBSNCOD_tfidf_2_x', 'TFT_STDBSNCOD_tfidf_3_x', 'TFT_STDBSNCOD_tfidf_4_x', 'TFT_STDBSNCOD_tfidf_6_x', 'TFT_STDBSNCOD_tfidf_9_x', 'TFT_STDBSNCOD_countvec_0_x', 'TFT_STDBSNCOD_countvec_3_x', 'TFT_STDBSNCOD_countvec_4_x', 'TFT_STDBSNCOD_countvec_6_x', 'TFT_STDBSNCOD_countvec_8_x', 'TFT_STDBSNCOD_countvec_9_x', 'TFT_CRPACC_nunique_mbank_trn_790437b5c04ac674519e99323691fcba', 'TFT_TRNAMT_mean_mbank_trn_790437b5c04ac674519e99323691fcba', 'TFT_TRNAMT_mean_mbank_trn_c5b1c177284d3a5fcb82a1421f7a4943', 'TFT_TRNAMT_max_mbank_trn_790437b5c04ac674519e99323691fcba', 'TFT_TRNAMT_max_mbank_trn_89e9b12804f2340183e639ef6c0e4fd7', 'TFT_TRNAMT_min_mbank_trn_790437b5c04ac674519e99323691fcba', 'TFT_TRNAMT_min_mbank_trn_89e9b12804f2340183e639ef6c0e4fd7', 'TFT_TRNAMT_std_mbank_trn_790437b5c04ac674519e99323691fcba', 'TFT_TRNAMT_ptp_mbank_trn_790437b5c04ac674519e99323691fcba', 'TFT_TRNAMT_sum_mbank_trn_790437b5c04ac674519e99323691fcba', 'TFT_TRNAMT_sum_mbank_trn_89e9b12804f2340183e639ef6c0e4fd7', 'TFT_STDBSNCOD_count_mbank_qry_4a50500443ba4972159b20abae2eb1d8', 'TFT_STDBSNCOD_count_mbank_qry_4fdf359ff1e29f59587f61df48c6763c', 'TFT_STDBSNCOD_count_mbank_qry_6f6af3fc3864671b3ccbf2093ecfc8ba', 'TFT_STDBSNCOD_count_mbank_qry_db0a93901176d46183168b40ff174b52', 'all_flw_特征值中心性', 'DAY_FA_BAL - MAVER_FA_BAL', 'DAY_FA_BAL / YAVER_FA_BAL', 'DAY_FA_BAL / DAY_AUM_BAL', 'DAY_FA_BAL - SAVER_AUM_BAL', 'DAY_FA_BAL - YAVER_TOT_IVST_BAL', 'DAY_FA_BAL - DPSA_BAL', 'DAY_FA_BAL / DPSA_BAL', 'DAY_FA_BAL - MAVER_DPSA_BAL', 'DAY_FA_BAL / MAVER_DPSA_BAL', 'DAY_FA_BAL - SAVER_DPSA_BAL', 'DAY_FA_BAL / SAVER_DPSA_BAL', 'DAY_FA_BAL - YAVER_DPSA_BAL', 'DAY_FA_BAL - MAVER_TD_BAL', 'DAY_FA_BAL - SAVER_TD_BAL', 'MAVER_FA_BAL / SAVER_FA_BAL', 'MAVER_FA_BAL / DAY_AUM_BAL', 'MAVER_FA_BAL / MAVER_AUM_BAL', 'MAVER_FA_BAL - TOT_IVST_BAL', 'MAVER_FA_BAL - YAVER_TOT_IVST_BAL', 'MAVER_FA_BAL / MAVER_DPSA_BAL', 'MAVER_FA_BAL - SAVER_DPSA_BAL', 'MAVER_FA_BAL - YAVER_DPSA_BAL', 'MAVER_FA_BAL - MAVER_TD_BAL', 'MAVER_FA_BAL - YAVER_TD_BAL', 'SAVER_FA_BAL - MAVER_TOT_IVST_BAL', 'SAVER_FA_BAL - SAVER_DPSA_BAL', 'SAVER_FA_BAL / YAVER_DPSA_BAL', 'SAVER_FA_BAL - MAVER_TD_BAL', 'YAVER_FA_BAL - DAY_AUM_BAL', 'YAVER_FA_BAL / DAY_AUM_BAL', 'YAVER_FA_BAL - MAVER_AUM_BAL', 'YAVER_FA_BAL - SAVER_AUM_BAL', 'YAVER_FA_BAL / SAVER_AUM_BAL', 'YAVER_FA_BAL / YAVER_AUM_BAL', 'YAVER_FA_BAL - MAVER_TOT_IVST_BAL', 'YAVER_FA_BAL / SAVER_TOT_IVST_BAL', 'YAVER_FA_BAL / DPSA_BAL', 'YAVER_FA_BAL - MAVER_DPSA_BAL', 'YAVER_FA_BAL / MAVER_DPSA_BAL', 'YAVER_FA_BAL - SAVER_DPSA_BAL', 'YAVER_FA_BAL - YAVER_TD_BAL', 'DAY_AUM_BAL - SAVER_TOT_IVST_BAL', 'DAY_AUM_BAL - YAVER_TOT_IVST_BAL', 'DAY_AUM_BAL / YAVER_TOT_IVST_BAL', 'DAY_AUM_BAL - DPSA_BAL', 'DAY_AUM_BAL - MAVER_DPSA_BAL', 'DAY_AUM_BAL / MAVER_DPSA_BAL', 'DAY_AUM_BAL - SAVER_DPSA_BAL', 'DAY_AUM_BAL / SAVER_DPSA_BAL', 'DAY_AUM_BAL - YAVER_DPSA_BAL', 'DAY_AUM_BAL - TD_BAL', 'DAY_AUM_BAL / TD_BAL', 'DAY_AUM_BAL - SAVER_TD_BAL', 'DAY_AUM_BAL - YAVER_TD_BAL', 'MAVER_AUM_BAL - TOT_IVST_BAL', 'MAVER_AUM_BAL - YAVER_TOT_IVST_BAL', 'MAVER_AUM_BAL - SAVER_DPSA_BAL', 'MAVER_AUM_BAL - YAVER_DPSA_BAL', 'MAVER_AUM_BAL / TD_BAL', 'MAVER_AUM_BAL - MAVER_TD_BAL', 'MAVER_AUM_BAL - SAVER_TD_BAL', 'MAVER_AUM_BAL - YAVER_TD_BAL', 'SAVER_AUM_BAL - SAVER_TOT_IVST_BAL', 'YAVER_AUM_BAL - SAVER_DPSA_BAL', 'YAVER_AUM_BAL - YAVER_DPSA_BAL', 'TOT_IVST_BAL - MAVER_TOT_IVST_BAL', 'TOT_IVST_BAL / DPSA_BAL', 'TOT_IVST_BAL - TD_BAL', 'TOT_IVST_BAL - MAVER_TD_BAL', 'MAVER_TOT_IVST_BAL / DPSA_BAL', 'MAVER_TOT_IVST_BAL - MAVER_DPSA_BAL', 'SAVER_TOT_IVST_BAL - DPSA_BAL', 'SAVER_TOT_IVST_BAL / DPSA_BAL', 'YAVER_TOT_IVST_BAL - DPSA_BAL', 'YAVER_TOT_IVST_BAL / DPSA_BAL', 'YAVER_TOT_IVST_BAL / MAVER_DPSA_BAL', 'DPSA_BAL - MAVER_TD_BAL', 'DPSA_BAL / MAVER_TD_BAL', 'MAVER_DPSA_BAL - YAVER_DPSA_BAL', 'SAVER_DPSA_BAL / TD_BAL', 'SAVER_DPSA_BAL / MAVER_TD_BAL', 'YAVER_DPSA_BAL - TD_BAL', 'YAVER_DPSA_BAL - SAVER_TD_BAL', 'MAVER_TD_BAL / SAVER_TD_BAL', 'TFT_TRNAMT_mean_mbank_trn_mamt_1_790437b5c04ac674519e99323691fcba', 'TFT_TRNAMT_mean_mbank_trn_mamt_3_790437b5c04ac674519e99323691fcba', 'TFT_TRNAMT_min_mbank_trn_mamt_1_790437b5c04ac674519e99323691fcba', 'TFT_TRNAMT_min_mbank_trn_mamt_3_790437b5c04ac674519e99323691fcba', 'TFT_TRNAMT_median_mbank_trn_mamt_3_790437b5c04ac674519e99323691fcba', 'TFT_TRNAMT_std_mbank_trn_mamt_1_790437b5c04ac674519e99323691fcba', 'TFT_TRNAMT_std_mbank_trn_mamt_3_790437b5c04ac674519e99323691fcba', 'TFT_TRNAMT_sum_mbank_trn_mamt_1_790437b5c04ac674519e99323691fcba', 'TFT_TRNAMT_sum_mbank_trn_mamt_3_790437b5c04ac674519e99323691fcba', 'TFT_TRNAMT_count_mbank_trn_mamt_1_790437b5c04ac674519e99323691fcba', 'TFT_TRNAMT_count_mbank_trn_mamt_3_790437b5c04ac674519e99323691fcba', 'TFT_TRNAMT_max_mbank_trn_wamt_0_790437b5c04ac674519e99323691fcba', 'TFT_TRNAMT_max_mbank_trn_wamt_1_790437b5c04ac674519e99323691fcba', 'TFT_TRNAMT_max_mbank_trn_wamt_2_790437b5c04ac674519e99323691fcba', 'TFT_TRNAMT_max_mbank_trn_wamt_4_790437b5c04ac674519e99323691fcba', 'TFT_TRNAMT_max_mbank_trn_wamt_6_790437b5c04ac674519e99323691fcba', 'TFT_TRNAMT_min_mbank_trn_wamt_4_790437b5c04ac674519e99323691fcba', 'TFT_TRNAMT_median_mbank_trn_wamt_2_790437b5c04ac674519e99323691fcba', 'TFT_TRNAMT_median_mbank_trn_wamt_3_790437b5c04ac674519e99323691fcba', 'TFT_TRNAMT_std_mbank_trn_wamt_1_790437b5c04ac674519e99323691fcba', 'TFT_TRNAMT_std_mbank_trn_wamt_3_790437b5c04ac674519e99323691fcba', 'TFT_TRNAMT_std_mbank_trn_wamt_4_790437b5c04ac674519e99323691fcba', 'TFT_TRNAMT_std_mbank_trn_wamt_6_790437b5c04ac674519e99323691fcba', 'TFT_TRNAMT_sum_mbank_trn_wamt_1_790437b5c04ac674519e99323691fcba', 'TFT_TRNAMT_sum_mbank_trn_wamt_4_790437b5c04ac674519e99323691fcba', 'TFT_TRNAMT_sum_mbank_trn_wamt_5_790437b5c04ac674519e99323691fcba', 'TFT_TRNAMT_sum_mbank_trn_wamt_6_790437b5c04ac674519e99323691fcba', 'TFT_CSTACC_count_mbank_trn_ycnt_790437b5c04ac674519e99323691fcba', 'TFT_CSTACC_count_mbank_trn_ycnt_89e9b12804f2340183e639ef6c0e4fd7', 'TFT_CSTACC_count_mbank_trn_mcnt_1_790437b5c04ac674519e99323691fcba', 'TFT_CSTACC_count_mbank_trn_mcnt_3_790437b5c04ac674519e99323691fcba', 'TFT_CRPACC_nunique_mbank_trn_wacccnt_1_790437b5c04ac674519e99323691fcba', 'TFT_CRPACC_count_mbank_trn_wacccnt_2_790437b5c04ac674519e99323691fcba', 'CUST_NO_count_mbank_qry_ycnt_6f6af3fc3864671b3ccbf2093ecfc8ba', 'CUST_NO_count_mbank_qry_ycnt_db0a93901176d46183168b40ff174b52', 'CUST_NO_count_mbank_qry_mcnt_1_6f6af3fc3864671b3ccbf2093ecfc8ba', 'CUST_NO_count_mbank_qry_mcnt_3_4a50500443ba4972159b20abae2eb1d8', 'CUST_NO_count_mbank_qry_mcnt_3_6f6af3fc3864671b3ccbf2093ecfc8ba', 'CUST_NO_count_mbank_qry_mcnt_3_db0a93901176d46183168b40ff174b52', 'CUST_NO_count_mbank_qry_mcnt_3_e481bae2b43d054e6afb6d6bf8760169', 'CUST_NO_count_mbank_qry_wcnt_0_4a50500443ba4972159b20abae2eb1d8', 'CUST_NO_count_mbank_qry_wcnt_0_e481bae2b43d054e6afb6d6bf8760169', 'CUST_NO_count_mbank_qry_wcnt_1_4a50500443ba4972159b20abae2eb1d8', 'CUST_NO_count_mbank_qry_wcnt_1_db0a93901176d46183168b40ff174b52', 'CUST_NO_count_mbank_qry_wcnt_2_6f6af3fc3864671b3ccbf2093ecfc8ba', 'CUST_NO_count_mbank_qry_wcnt_3_4a50500443ba4972159b20abae2eb1d8', 'CUST_NO_count_mbank_qry_wcnt_3_db0a93901176d46183168b40ff174b52', 'CUST_NO_count_mbank_qry_wcnt_6_4a50500443ba4972159b20abae2eb1d8', 'DPFAPROD_SUM_LR', 'DPFAPROD_SUM_linear', 'TAGS_PROD_LR', 'TAGS_PROD_LR_linear', 'DP_CUST_LR', 'DP_CUST_LR_linear', 'CUST_FA_LR', 'CUST_FA_linear', 'TFT_CSTNOQRYTRNFLWmonth_mean', 'TFT_CSTNOQRYTRNFLWmonth_std', 'TFT_CSTNOQRYTRNFLWmonth_median', 'TFT_CSTNOQRYTRNFLWmonth_sum', 'TFT_CSTNOQRYTRNFLWday_sum', 'TFT_CSTNOQRYTRNFLWhour_min', 'TFT_CSTNOQRYTRNFLWhour_mean', 'TFT_CSTNOQRYTRNFLWhour_median', 'TFT_CSTNOQRYTRNFLWhour_nunique', 'TFT_CSTNOQRYTRNFLWminute_nunique', 'TFT_STDBSNCOD_tfidf_0_y', 'TFT_STDBSNCOD_tfidf_1_y', 'TFT_STDBSNCOD_tfidf_2_y', 'TFT_STDBSNCOD_tfidf_3_y', 'TFT_STDBSNCOD_tfidf_6_y', 'TFT_STDBSNCOD_tfidf_7_y', 'TFT_STDBSNCOD_tfidf_9_y', 'TFT_STDBSNCOD_countvec_0_y', 'TFT_STDBSNCOD_countvec_1_y', 'TFT_STDBSNCOD_countvec_2_y', 'TFT_STDBSNCOD_countvec_4_y', 'TFT_STDBSNCOD_countvec_7_y', 'TFT_STDBSNCOD_countvec_9_y', 'max_amt_days_to_now_0_in', 'recent_days_to_now_1_in', 'APSDPRDNO_date_months_to_now_APSDTRAMT_mean_0_in', 'APSDPRDNO_date_months_to_now_APSDTRAMT_mean_1_in', 'APSDPRDNO_date_months_to_now_APSDTRAMT_max_0_in', 'APSDPRDNO_date_months_to_now_APSDTRAMT_median_0_in', 'APSDPRDNO_date_months_to_now_APSDTRAMT_median_1_in', 'APSDPRDNO_date_months_to_now_APSDTRAMT_std_0_in', 'APSDPRDNO_date_months_to_now_APSDTRAMT_kurt_0_in', 'APSDPRDNO_date_months_to_now_APSDTRAMT_kurt_1_in', 'APSDPRDNO_date_months_to_now_count_sum_0_in', 'APSDPRDNO_date_weeks_to_now_APSDTRAMT_mean_1_in', 'APSDPRDNO_date_weeks_to_now_APSDTRAMT_mean_4_in', 'APSDPRDNO_date_weeks_to_now_APSDTRAMT_mean_6_in', 'APSDPRDNO_date_weeks_to_now_APSDTRAMT_max_1_in', 'APSDPRDNO_date_weeks_to_now_APSDTRAMT_max_6_in', 'APSDPRDNO_date_weeks_to_now_APSDTRAMT_min_1_in', 'APSDPRDNO_date_weeks_to_now_APSDTRAMT_min_2_in', 'APSDPRDNO_date_weeks_to_now_APSDTRAMT_median_0_in', 'APSDPRDNO_date_weeks_to_now_APSDTRAMT_median_4_in', 'APSDPRDNO_date_weeks_to_now_APSDTRAMT_std_0_in', 'APSDPRDNO_date_weeks_to_now_APSDTRAMT_std_3_in', 'APSDPRDNO_date_weeks_to_now_APSDTRAMT_std_4_in', 'APSDPRDNO_date_weeks_to_now_APSDTRAMT_std_5_in', 'APSDPRDNO_date_weeks_to_now_APSDTRAMT_std_7_in', 'APSDPRDNO_date_weeks_to_now_APSDTRAMT_std_8_in', 'APSDPRDNO_date_weeks_to_now_APSDTRAMT_sum_0_in', 'APSDPRDNO_date_weeks_to_now_APSDTRAMT_sum_6_in', 'APSDPRDNO_date_weeks_to_now_APSDTRAMT_skew_2_in', 'APSDPRDNO_date_weeks_to_now_APSDTRAMT_kurt_0_in', 'APSDPRDNO_date_weeks_to_now_count_sum_1_in', 'APSDPRDNO_date_weeks_to_now_count_sum_2_in', 'APSDPRDNO_date_weeks_to_now_count_sum_6_in', 'APSDPRDNO_date_weeks_to_now_APSDTRCOD_nunique_8_in', 'APSDPRDNO_date_weeks_to_now_APSDTRCHL_nunique_4_in', 'APSDPRDNO_date_weeks_to_now_APSDTRCHL_nunique_6_in', 'APSDPRDNO_APSDTRCHL_APSDTRAMT_mean_1d1fe13c960cff7c6f9dedeeb23a7524_in', 'APSDPRDNO_APSDTRCHL_APSDTRAMT_mean_e0457a5f5527aa8db06ede414933fecd_in', 'APSDPRDNO_APSDTRCHL_APSDTRAMT_max_1d1fe13c960cff7c6f9dedeeb23a7524_in', 'APSDPRDNO_APSDTRCHL_APSDTRAMT_min_e0457a5f5527aa8db06ede414933fecd_in', 'APSDPRDNO_APSDTRCHL_APSDTRAMT_median_0e30a5a85d29c720619212ed0721f096_in', 'APSDPRDNO_APSDTRCHL_APSDTRAMT_median_e0457a5f5527aa8db06ede414933fecd_in', 'APSDPRDNO_APSDTRCHL_APSDTRAMT_std_1d1fe13c960cff7c6f9dedeeb23a7524_in', 'APSDPRDNO_APSDTRCHL_APSDTRAMT_std_c203106fbee9e0c716712d29be26b9eb_in', 'APSDPRDNO_APSDTRCHL_APSDTRAMT_sum_e0457a5f5527aa8db06ede414933fecd_in', 'APSDPRDNO_APSDTRCHL_APSDTRAMT_skew_c203106fbee9e0c716712d29be26b9eb_in', 'APSDPRDNO_APSDTRCHL_APSDPRDNO_count_6d9345571f7032b7281da7f93d1ffb22_in', 'APSDPRDNO_APSDTRCHL_APSDPRDNO_count_833c893caec34ea7a6ad91fc874504e1_in', 'APSDPRDNO_APSDTRCHL_APSDPRDNO_count_bf092e346913a61d07b83ae4ced3e1ab_in', 'APSDPRDNO_APSDTRCHL_APSDPRDNO_count_e0457a5f5527aa8db06ede414933fecd_in', 'APSDPRDNO_APSDTRCHL_APSDPRDNO_count_6d9345571f7032b7281da7f93d1ffb22_div_tr_freq_in', 'APSDPRDNO_APSDTRCHL_APSDPRDNO_count_833c893caec34ea7a6ad91fc874504e1_div_tr_freq_in', 'APSDPRDNO_APSDTRCHL_APSDPRDNO_count_8c9c388064b85b10604063ea2f015e7b_div_tr_freq_in', 'APSDPRDNO_APSDTRCHL_APSDPRDNO_count_bf092e346913a61d07b83ae4ced3e1ab_div_tr_freq_in', 'APSDPRDNO_APSDTRCHL_APSDPRDNO_count_e0457a5f5527aa8db06ede414933fecd_div_tr_freq_in', 'recent_days_to_now_1_out', 'APSDPRDNO_date_months_to_now_APSDTRAMT_mean_0_out', 'APSDPRDNO_date_months_to_now_APSDTRAMT_max_1_out', 'APSDPRDNO_date_months_to_now_APSDTRAMT_min_0_out', 'APSDPRDNO_date_months_to_now_APSDTRAMT_median_1_out', 'APSDPRDNO_date_months_to_now_APSDTRAMT_std_0_out', 'APSDPRDNO_date_months_to_now_APSDTRAMT_sum_0_out', 'APSDPRDNO_date_weeks_to_now_APSDTRAMT_mean_0_out', 'APSDPRDNO_date_weeks_to_now_APSDTRAMT_mean_2_out', 'APSDPRDNO_date_weeks_to_now_APSDTRAMT_max_0_out', 'APSDPRDNO_date_weeks_to_now_APSDTRAMT_max_1_out', 'APSDPRDNO_date_weeks_to_now_APSDTRAMT_max_2_out', 'APSDPRDNO_date_weeks_to_now_APSDTRAMT_max_5_out', 'APSDPRDNO_date_weeks_to_now_APSDTRAMT_min_0_out', 'APSDPRDNO_date_weeks_to_now_APSDTRAMT_min_1_out', 'APSDPRDNO_date_weeks_to_now_APSDTRAMT_std_7_out', 'APSDPRDNO_date_weeks_to_now_APSDTRAMT_sum_0_out', 'APSDPRDNO_date_weeks_to_now_APSDTRAMT_sum_4_out', 'APSDPRDNO_date_weeks_to_now_APSDTRAMT_skew_0_out', 'APSDPRDNO_date_weeks_to_now_APSDTRAMT_skew_1_out', 'APSDPRDNO_date_weeks_to_now_APSDTRAMT_skew_2_out', 'APSDPRDNO_date_weeks_to_now_APSDTRAMT_skew_5_out', 'APSDPRDNO_date_weeks_to_now_APSDTRAMT_skew_8_out', 'APSDPRDNO_date_weeks_to_now_APSDTRAMT_kurt_0_out', 'APSDPRDNO_date_weeks_to_now_APSDTRAMT_kurt_2_out', 'APSDPRDNO_date_weeks_to_now_APSDTRAMT_kurt_4_out', 'APSDPRDNO_date_weeks_to_now_APSDTRAMT_kurt_5_out', 'APSDPRDNO_date_weeks_to_now_APSDTRAMT_kurt_8_out', 'APSDPRDNO_date_weeks_to_now_count_sum_1_out', 'APSDPRDNO_date_weeks_to_now_count_sum_4_out', 'APSDPRDNO_date_weeks_to_now_count_sum_8_out', 'APSDPRDNO_date_weeks_to_now_APSDTRCOD_nunique_8_out', 'APSDPRDNO_date_weeks_to_now_APSDTRCHL_nunique_8_out', 'APSDPRDNO_APSDTRCHL_APSDTRAMT_mean_c203106fbee9e0c716712d29be26b9eb_out', 'APSDPRDNO_APSDTRCHL_APSDTRAMT_max_0e30a5a85d29c720619212ed0721f096_out', 'APSDPRDNO_APSDTRCHL_APSDTRAMT_max_e0457a5f5527aa8db06ede414933fecd_out', 'APSDPRDNO_APSDTRCHL_APSDTRAMT_min_c203106fbee9e0c716712d29be26b9eb_out', 'APSDPRDNO_APSDTRCHL_APSDTRAMT_std_e0457a5f5527aa8db06ede414933fecd_out', 'APSDPRDNO_APSDTRCHL_APSDTRAMT_sum_c203106fbee9e0c716712d29be26b9eb_out', 'APSDPRDNO_APSDTRCHL_APSDTRAMT_kurt_0e30a5a85d29c720619212ed0721f096_out', 'APSDPRDNO_APSDTRCHL_APSDPRDNO_count_9bceb5d80bb37c1c18a5be002c323b58_out', 'APSDPRDNO_APSDTRCHL_APSDPRDNO_count_d42ebe89d9717253a6bcdce3f18e86cb_out', 'APSDPRDNO_APSDTRCHL_APSDPRDNO_count_f1011e59bf940b1141498087caf466d6_out', 'APSDPRDNO_APSDTRCHL_APSDPRDNO_count_01e0ba6a59386b8afd8f24849fb0cfa1_div_tr_freq_out', 'APSDPRDNO_APSDTRCHL_APSDPRDNO_count_0e30a5a85d29c720619212ed0721f096_div_tr_freq_out', 'APSDPRDNO_APSDTRCHL_APSDPRDNO_count_55246355778cf140b52a5c9f30092cfd_div_tr_freq_out', 'APSDPRDNO_APSDTRCHL_APSDPRDNO_count_5c3119f2ff725daf47462fca158997e2_div_tr_freq_out', 'APSDPRDNO_APSDTRCHL_APSDPRDNO_count_9bceb5d80bb37c1c18a5be002c323b58_div_tr_freq_out', 'APSDPRDNO_APSDTRCHL_APSDPRDNO_count_d42ebe89d9717253a6bcdce3f18e86cb_div_tr_freq_out', 'APSDPRDNO_APSDTRCHL_APSDPRDNO_count_f1011e59bf940b1141498087caf466d6_div_tr_freq_out', 'APSDPRDNO_date_months_to_now_APSDTRAMT_mean_1_net', 'APSDPRDNO_date_months_to_now_APSDTRAMT_std_0_net', 'APSDPRDNO_date_months_to_now_APSDTRAMT_kurt_0_net', 'APSDPRDNO_date_weeks_to_now_APSDTRAMT_mean_4_net', 'APSDPRDNO_date_weeks_to_now_APSDTRAMT_max_1_net', 'APSDPRDNO_date_weeks_to_now_APSDTRAMT_max_5_net', 'APSDPRDNO_date_weeks_to_now_APSDTRAMT_min_1_net', 'APSDPRDNO_date_weeks_to_now_APSDTRAMT_median_8_net', 'APSDPRDNO_date_weeks_to_now_APSDTRAMT_std_2_net', 'APSDPRDNO_date_weeks_to_now_APSDTRAMT_std_8_net', 'APSDPRDNO_date_weeks_to_now_APSDTRAMT_sum_8_net', 'APSDPRDNO_date_weeks_to_now_APSDTRAMT_skew_1_net', 'APSDPRDNO_date_weeks_to_now_APSDTRAMT_skew_2_net', 'APSDPRDNO_date_weeks_to_now_APSDTRAMT_skew_4_net', 'APSDPRDNO_date_weeks_to_now_APSDTRAMT_kurt_0_net', 'APSDPRDNO_date_weeks_to_now_APSDTRAMT_kurt_1_net', 'APSDPRDNO_date_weeks_to_now_APSDTRAMT_kurt_2_net', 'aps_APSDPRDNO_date_months_to_now_strange_amts_mean_0', 'aps_APSDPRDNO_date_months_to_now_strange_amts_std_0', 'aps_APSDPRDNO_date_months_to_now_strange_amts_sum_0', 'aps_APSDPRDNO_date_months_to_now_strange_amts_kurt_0', 'aps_APSDPRDNO_date_months_to_now_strange_cpts_mean_0', 'aps_APSDPRDNO_date_months_to_now_strange_cpts_max_0', 'aps_APSDPRDNO_date_months_to_now_strange_cpts_median_0', 'aps_APSDPRDNO_date_months_to_now_strange_cpts_std_0', 'aps_APSDPRDNO_date_months_to_now_strange_cpts_sum_0', 'aps_APSDPRDNO_date_months_to_now_strange_cpts_kurt_0', 'aps_APSDPRDNO_date_months_to_now_count_strange_mean_0', 'aps_APSDPRDNO_date_months_to_now_count_strange_min_0', 'aps_APSDPRDNO_date_months_to_now_count_strange_median_0', 'aps_APSDPRDNO_date_months_to_now_count_strange_std_0', 'aps_APSDPRDNO_date_months_to_now_count_strange_sum_0', 'aps_APSDPRDNO_date_months_to_now_count_strange_kurt_0', 'aps_APSDPRDNO_date_weeks_to_now_strange_amts_mean_0', 'aps_APSDPRDNO_date_weeks_to_now_strange_amts_mean_1', 'aps_APSDPRDNO_date_weeks_to_now_strange_amts_mean_3', 'aps_APSDPRDNO_date_weeks_to_now_strange_amts_max_3', 'aps_APSDPRDNO_date_weeks_to_now_strange_amts_min_2', 'aps_APSDPRDNO_date_weeks_to_now_strange_amts_median_0', 'aps_APSDPRDNO_date_weeks_to_now_strange_amts_median_1', 'aps_APSDPRDNO_date_weeks_to_now_strange_amts_median_3', 'aps_APSDPRDNO_date_weeks_to_now_strange_cpts_mean_0', 'aps_APSDPRDNO_date_weeks_to_now_strange_cpts_max_0', 'aps_APSDPRDNO_date_weeks_to_now_strange_cpts_min_0', 'aps_APSDPRDNO_date_weeks_to_now_strange_cpts_sum_0', 'aps_APSDPRDNO_date_weeks_to_now_strange_cpts_sum_1', 'aps_APSDPRDNO_date_weeks_to_now_count_strange_mean_3', 'aps_APSDPRDNO_date_weeks_to_now_count_strange_max_2', 'aps_APSDPRDNO_date_weeks_to_now_count_strange_median_3', 'aps_APSDPRDNO_date_weeks_to_now_count_strange_sum_0', 'aps_APSDPRDNO_date_weeks_to_now_count_strange_sum_1', 'aps_APSDPRDNO_APSDTRCHL_strange_amts_mean_e0457a5f5527aa8db06ede414933fecd', 'aps_APSDPRDNO_APSDTRCHL_strange_amts_max_e0457a5f5527aa8db06ede414933fecd', 'aps_APSDPRDNO_APSDTRCHL_strange_amts_min_e0457a5f5527aa8db06ede414933fecd', 'aps_APSDPRDNO_APSDTRCHL_strange_amts_median_e0457a5f5527aa8db06ede414933fecd', 'aps_APSDPRDNO_APSDTRCHL_strange_amts_sum_e0457a5f5527aa8db06ede414933fecd', 'APSDPRDNO_APSDTRCHL_APSDPRDNO_count_833c893caec34ea7a6ad91fc874504e1_empty', 'APSDPRDNO_APSDTRCHL_APSDPRDNO_count_c203106fbee9e0c716712d29be26b9eb_empty', 'APSDPRDNO_APSDTRCHL_APSDPRDNO_count_833c893caec34ea7a6ad91fc874504e1_div_tr_freq_empty', 'aps_data0414_0515nunique', 'aps_money_B0_data0414_0515sum', 'aps_money_B0_data0515_0615sum', 'aps_money_S0_data0515_0615sum', 'aps_qz_APSDTRCHL_max_data0414_0515count', 'outaps_apsdtrchl_all_rate_data0414_0515', 'inaps_APSDTRCHL_max_apsdtrchl_all_rate_data0414_0515', 'outaps_APSDTRCHL_two_apsdtrchl_all_rate_data0414_0515', 'inaps_APSDTRCHL_three_apsdtrchl_all_rate_data0515_0615', 'mbank_trnflw_TFT_STDBSNCOD_data0515_0615nunique', 'mbank_trnflw_TFT_TRNAMT_data0515_0615sum', 'mbank_trnflw_TFT_CRPACC_data0515_0615nunique', 'mbank_trnflw_TFT_STDBSNCOD_data0414_0515count', 'mbank_trnflw_TFT_STDBSNCOD_data0515_0615count', 'mbank_trnflw_TFT_STDBSNCOD_max_data0515_0615count', 'mbank_qrytrnflw_TFT_STDBSNCOD_data0515_0615nunique', 'mbank_qrytrnflw_TFT_STDBSNCOD_max_data0515_0615count', 'mbank_qrytrnflw_TFT_STDBSNCOD_two_data0414_0515count', 'mbank_qrytrnflw_TFT_STDBSNCOD_two_data0515_0615count', 'aps_data0414_0421nunique', 'aps_data0526_0602nunique', 'aps_money_S0_data0519_0526sum', 'aps_money_S0_data0526_0602sum', 'aps_APSDTRCOD_data0428_0505nunique', 'aps_APSDTRCOD_data0505_0512nunique', 'aps_APSDTRCOD_data0512_0519nunique', 'aps_APSDTRCOD_data0519_0526nunique', 'aps_APSDTRCOD_data0602_0609nunique', 'aps_APSDTRCOD_data0526_0602count', 'aps_qz_APSDTRCOD_two_data0526_0602count', 'aps_qz_APSDTRCOD_three_data0414_0421count', 'aps_qz_APSDTRCHL_max_data0414_0421count', 'aps_qz_APSDTRCHL_max_data0519_0526count', 'inaps_APSDTRCHL_max_apsdtrchl_all_rate_data0428_0505', 'outaps_APSDTRCHL_max_apsdtrchl_all_rate_data0428_0505', 'inaps_APSDTRCHL_max_apsdtrchl_all_rate_data0512_0519', 'outaps_APSDTRCHL_max_apsdtrchl_all_rate_data0512_0519', 'inaps_APSDTRCHL_two_apsdtrchl_all_rate_data0428_0505', 'inaps_APSDTRCHL_two_apsdtrchl_all_rate_data0505_0512', 'outaps_APSDTRCHL_two_apsdtrchl_all_rate_data0505_0512', 'inaps_APSDTRCHL_two_apsdtrchl_all_rate_data0519_0526', 'outaps_APSDTRCHL_three_apsdtrchl_all_rate_data0428_0505', 'mbank_trnflw_TFT_STDBSNCOD_data0526_0602nunique', 'mbank_trnflw_TFT_TRNAMT_data0512_0519sum', 'mbank_trnflw_TFT_TRNAMT_data0519_0526sum', 'mbank_trnflw_TFT_TRNAMT_data0526_0602sum', 'mbank_trnflw_TFT_TRNAMT_data0602_0609sum', 'mbank_trnflw_TFT_CRPACC_data0421_0428nunique', 'mbank_trnflw_TFT_CRPACC_data0519_0526nunique', 'mbank_trnflw_TFT_CRPACC_data0526_0602nunique', 'mbank_trnflw_TFT_STDBSNCOD_data0512_0519count', 'mbank_trnflw_TFT_STDBSNCOD_data0526_0602count', 'mbank_trnflw_TFT_STDBSNCOD_data0602_0609count', 'mbank_trnflw_TFT_STDBSNCOD_max_data0421_0428count', 'mbank_qrytrnflw_TFT_STDBSNCOD_data0512_0519nunique', 'mbank_qrytrnflw_TFT_STDBSNCOD_data0414_0421count', 'mbank_qrytrnflw_TFT_STDBSNCOD_data0505_0512count', 'mbank_qrytrnflw_TFT_STDBSNCOD_data0526_0602count', 'mbank_qrytrnflw_TFT_STDBSNCOD_max_data0414_0421count', 'mbank_qrytrnflw_TFT_STDBSNCOD_max_data0428_0505count', 'mbank_qrytrnflw_TFT_STDBSNCOD_max_data0519_0526count', 'mbank_qrytrnflw_TFT_STDBSNCOD_two_data0421_0428count', 'mbank_qrytrnflw_TFT_STDBSNCOD_three_data0428_0505count', 'aps_night_00_06_data0414_0515nunique', 'aps_night_00_06_data0414_0515sum', 'aps_money_B0_night_00_06_data0414_0515sum', 'aps_money_S0_night_00_06_data0515_0615sum', 'aps_APSDTRCOD_night_00_06_data0414_0515nunique', 'aps_APSDTRCOD_night_00_06_data0515_0615nunique', 'aps_APSDTRCOD_night_00_06_data0515_0615count', 'aps_qz_APSDTRCOD_two_night_00_06_data0414_0515count', 'aps_qz_APSDTRCHL_two_night_00_06_data0414_0515count', 'aps_qz_APSDTRCHL_two_night_00_06_data0515_0615count', 'inaps_apsdtrchl_all_rate_night_00_06_data0515_0615', 'inaps_APSDTRCHL_max_apsdtrchl_all_rate_night_00_06_data0515_0615', 'inaps_APSDTRCHL_two_apsdtrchl_all_rate_night_00_06_data0414_0515', 'inaps_APSDTRCHL_two_apsdtrchl_all_rate_night_00_06_data0515_0615', 'mbank_qrytrnflw_TFT_STDBSNCOD_night_00_06_data0414_0515nunique', 'mbank_qrytrnflw_TFT_STDBSNCOD_night_00_06_data0515_0615count', 'mbank_qrytrnflw_TFT_STDBSNCOD_two_night_00_06_data0414_0515count', 'mbank_qrytrnflw_TFT_STDBSNCOD_two_night_00_06_data0515_0615count', 'aps_night_00_06_data0505_0512sum', 'aps_money_S0_night_00_06_data0505_0512sum', 'aps_APSDTRCOD_night_00_06_data0519_0526nunique', 'inaps_apsdtrchl_all_rate_night_00_06_data0526_0602', 'outaps_apsdtrchl_all_rate_night_00_06_data0526_0602', 'inaps_APSDTRCHL_max_apsdtrchl_all_rate_night_00_06_data0526_0602', 'inaps_APSDTRCHL_two_apsdtrchl_all_rate_night_00_06_data0428_0505', 'outaps_APSDTRCHL_two_apsdtrchl_all_rate_night_00_06_data0519_0526', 'aps_money_B0_night_06_18_data0515_0615sum', 'aps_APSDTRCOD_night_06_18_data0414_0515count', 'aps_APSDTRCOD_night_06_18_data0515_0615count', 'aps_qz_APSDTRCOD_two_night_06_18_data0515_0615count', 'aps_qz_APSDTRCHL_max_night_06_18_data0515_0615count', 'inaps_apsdtrchl_all_rate_night_06_18_data0515_0615', 'inaps_APSDTRCHL_max_apsdtrchl_all_rate_night_06_18_data0515_0615', 'mbank_trnflw_TFT_TRNAMT_night_06_18_data0414_0515sum', 'mbank_trnflw_TFT_TRNAMT_night_06_18_data0515_0615sum', 'mbank_trnflw_TFT_CRPACC_night_06_18_data0414_0515nunique', 'mbank_trnflw_TFT_CRPACC_night_06_18_data0515_0615nunique', 'mbank_trnflw_TFT_STDBSNCOD_night_06_18_data0515_0615count', 'mbank_trnflw_TFT_STDBSNCOD_max_night_06_18_data0515_0615count', 'mbank_qrytrnflw_TFT_STDBSNCOD_night_06_18_data0515_0615nunique', 'mbank_qrytrnflw_TFT_STDBSNCOD_night_06_18_data0515_0615count', 'mbank_qrytrnflw_TFT_STDBSNCOD_max_night_06_18_data0515_0615count', 'mbank_qrytrnflw_TFT_STDBSNCOD_two_night_06_18_data0515_0615count', 'mbank_qrytrnflw_TFT_STDBSNCOD_three_night_06_18_data0515_0615count', 'aps_night_06_18_data0519_0526nunique', 'aps_night_06_18_data0602_0609nunique', 'aps_money_S0_night_06_18_data0414_0421sum', 'aps_money_S0_night_06_18_data0428_0505sum', 'aps_money_S0_night_06_18_data0526_0602sum', 'aps_APSDTRCOD_night_06_18_data0428_0505count', 'aps_APSDTRCOD_night_06_18_data0519_0526count', 'aps_qz_APSDTRCOD_max_night_06_18_data0414_0421count', 'aps_qz_APSDTRCOD_max_night_06_18_data0505_0512count', 'aps_qz_APSDTRCOD_max_night_06_18_data0512_0519count', 'aps_qz_APSDTRCOD_two_night_06_18_data0421_0428count', 'aps_qz_APSDTRCOD_two_night_06_18_data0602_0609count', 'aps_qz_APSDTRCOD_three_night_06_18_data0526_0602count', 'aps_qz_APSDTRCHL_max_night_06_18_data0421_0428count', 'aps_qz_APSDTRCHL_max_night_06_18_data0512_0519count', 'aps_qz_APSDTRCHL_max_night_06_18_data0519_0526count', 'aps_qz_APSDTRCHL_max_night_06_18_data0602_0609count', 'aps_qz_APSDTRCHL_three_night_06_18_data0505_0512count', 'aps_qz_APSDTRCHL_three_night_06_18_data0512_0519count', 'outaps_APSDTRCHL_max_apsdtrchl_all_rate_night_06_18_data0421_0428', 'inaps_APSDTRCHL_max_apsdtrchl_all_rate_night_06_18_data0512_0519', 'outaps_APSDTRCHL_max_apsdtrchl_all_rate_night_06_18_data0512_0519', 'outaps_APSDTRCHL_max_apsdtrchl_all_rate_night_06_18_data0519_0526', 'outaps_APSDTRCHL_two_apsdtrchl_all_rate_night_06_18_data0428_0505', 'outaps_APSDTRCHL_two_apsdtrchl_all_rate_night_06_18_data0505_0512', 'outaps_APSDTRCHL_two_apsdtrchl_all_rate_night_06_18_data0602_0609', 'outaps_APSDTRCHL_three_apsdtrchl_all_rate_night_06_18_data0428_0505', 'mbank_trnflw_TFT_STDBSNCOD_night_06_18_data0512_0519nunique', 'mbank_trnflw_TFT_TRNAMT_night_06_18_data0414_0421sum', 'mbank_trnflw_TFT_TRNAMT_night_06_18_data0421_0428sum', 'mbank_trnflw_TFT_TRNAMT_night_06_18_data0512_0519sum', 'mbank_trnflw_TFT_TRNAMT_night_06_18_data0519_0526sum', 'mbank_trnflw_TFT_TRNAMT_night_06_18_data0602_0609sum', 'mbank_trnflw_TFT_CRPACC_night_06_18_data0428_0505nunique', 'mbank_trnflw_TFT_CRPACC_night_06_18_data0505_0512nunique', 'mbank_trnflw_TFT_CRPACC_night_06_18_data0602_0609nunique', 'mbank_trnflw_TFT_STDBSNCOD_max_night_06_18_data0421_0428count', 'mbank_trnflw_TFT_STDBSNCOD_max_night_06_18_data0428_0505count', 'mbank_trnflw_TFT_STDBSNCOD_max_night_06_18_data0505_0512count', 'mbank_trnflw_TFT_STDBSNCOD_max_night_06_18_data0519_0526count', 'mbank_qrytrnflw_TFT_STDBSNCOD_night_06_18_data0526_0602nunique', 'mbank_qrytrnflw_TFT_STDBSNCOD_night_06_18_data0428_0505count', 'mbank_qrytrnflw_TFT_STDBSNCOD_night_06_18_data0526_0602count', 'mbank_qrytrnflw_TFT_STDBSNCOD_max_night_06_18_data0526_0602count', 'mbank_qrytrnflw_TFT_STDBSNCOD_two_night_06_18_data0421_0428count', 'mbank_qrytrnflw_TFT_STDBSNCOD_two_night_06_18_data0526_0602count', 'mbank_qrytrnflw_TFT_STDBSNCOD_two_night_06_18_data0602_0609count', 'mbank_qrytrnflw_TFT_STDBSNCOD_three_night_06_18_data0428_0505count', 'aps_night_18_06_data0515_0615sum', 'aps_money_B0_night_18_06_data0515_0615sum', 'aps_qz_APSDTRCOD_two_night_18_06_data0515_0615count', 'aps_qz_APSDTRCOD_three_night_18_06_data0515_0615count', 'inaps_apsdtrchl_all_rate_night_18_06_data0515_0615', 'outaps_APSDTRCHL_three_apsdtrchl_all_rate_night_18_06_data0414_0515', 'mbank_trnflw_TFT_TRNAMT_night_18_06_data0414_0515sum', 'mbank_trnflw_TFT_STDBSNCOD_night_18_06_data0515_0615count', 'mbank_qrytrnflw_TFT_STDBSNCOD_night_18_06_data0414_0515nunique', 'mbank_qrytrnflw_TFT_STDBSNCOD_night_18_06_data0515_0615nunique', 'mbank_qrytrnflw_TFT_STDBSNCOD_night_18_06_data0414_0515count', 'mbank_qrytrnflw_TFT_STDBSNCOD_max_night_18_06_data0414_0515count', 'mbank_qrytrnflw_TFT_STDBSNCOD_max_night_18_06_data0515_0615count', 'mbank_qrytrnflw_TFT_STDBSNCOD_three_night_18_06_data0515_0615count', 'aps_night_18_06_data0421_0428nunique', 'aps_night_18_06_data0414_0421sum', 'aps_night_18_06_data0519_0526sum', 'aps_money_B0_night_18_06_data0428_0505sum', 'aps_money_B0_night_18_06_data0526_0602sum', 'aps_money_S0_night_18_06_data0421_0428sum', 'aps_money_S0_night_18_06_data0428_0505sum', 'aps_money_S0_night_18_06_data0526_0602sum', 'aps_APSDTRCOD_night_18_06_data0526_0602nunique', 'aps_APSDTRCOD_night_18_06_data0421_0428count', 'aps_APSDTRCOD_night_18_06_data0512_0519count', 'aps_qz_APSDTRCOD_two_night_18_06_data0505_0512count', 'aps_qz_APSDTRCOD_two_night_18_06_data0512_0519count', 'aps_qz_APSDTRCHL_max_night_18_06_data0421_0428count', 'aps_qz_APSDTRCHL_max_night_18_06_data0512_0519count', 'inaps_apsdtrchl_all_rate_night_18_06_data0428_0505', 'inaps_apsdtrchl_all_rate_night_18_06_data0505_0512', 'outaps_apsdtrchl_all_rate_night_18_06_data0602_0609', 'inaps_APSDTRCHL_max_apsdtrchl_all_rate_night_18_06_data0414_0421', 'inaps_APSDTRCHL_max_apsdtrchl_all_rate_night_18_06_data0512_0519', 'outaps_APSDTRCHL_max_apsdtrchl_all_rate_night_18_06_data0526_0602', 'outaps_APSDTRCHL_two_apsdtrchl_all_rate_night_18_06_data0414_0421', 'inaps_APSDTRCHL_two_apsdtrchl_all_rate_night_18_06_data0428_0505', 'outaps_APSDTRCHL_two_apsdtrchl_all_rate_night_18_06_data0428_0505', 'outaps_APSDTRCHL_two_apsdtrchl_all_rate_night_18_06_data0505_0512', 'inaps_APSDTRCHL_three_apsdtrchl_all_rate_night_18_06_data0602_0609', 'mbank_trnflw_TFT_STDBSNCOD_night_18_06_data0428_0505nunique', 'mbank_trnflw_TFT_TRNAMT_night_18_06_data0505_0512sum', 'mbank_trnflw_TFT_STDBSNCOD_night_18_06_data0505_0512count', 'mbank_trnflw_TFT_STDBSNCOD_night_18_06_data0519_0526count', 'mbank_qrytrnflw_TFT_STDBSNCOD_night_18_06_data0428_0505nunique', 'mbank_qrytrnflw_TFT_STDBSNCOD_night_18_06_data0505_0512nunique', 'mbank_qrytrnflw_TFT_STDBSNCOD_night_18_06_data0526_0602nunique', 'mbank_qrytrnflw_TFT_STDBSNCOD_night_18_06_data0428_0505count', 'mbank_qrytrnflw_TFT_STDBSNCOD_max_night_18_06_data0505_0512count', 'mbank_qrytrnflw_TFT_STDBSNCOD_max_night_18_06_data0526_0602count', 'mbank_qrytrnflw_TFT_STDBSNCOD_max_night_18_06_data0602_0609count', 'mbank_qrytrnflw_TFT_STDBSNCOD_two_night_18_06_data0519_0526count', 'in_apsdtrchl_all_rate', 'out_apsdtrchl_all_rate', 'in_apsdtrchltwo_all_rate', 'out_apsdtrchltwo_all_rate', 'NTRL_CUST_SEX_CD_FLAG_mean']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-07T06:47:39.120374Z",
     "iopub.status.busy": "2023-11-07T06:47:39.120198Z",
     "iopub.status.idle": "2023-11-07T06:47:40.062580Z",
     "shell.execute_reply": "2023-11-07T06:47:40.061914Z",
     "shell.execute_reply.started": "2023-11-07T06:47:39.120353Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#feature_name=used_feat1\n",
    "X_train = df_train[feature_name].reset_index(drop=True)\n",
    "X_test = df_test[feature_name].reset_index(drop=True)\n",
    "y = df_train['FLAG'].reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-07T06:47:40.063743Z",
     "iopub.status.busy": "2023-11-07T06:47:40.063536Z",
     "iopub.status.idle": "2023-11-07T06:47:40.067567Z",
     "shell.execute_reply": "2023-11-07T06:47:40.067074Z",
     "shell.execute_reply.started": "2023-11-07T06:47:40.063718Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5089, 1067)"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape\n",
    "X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-07T06:47:40.068632Z",
     "iopub.status.busy": "2023-11-07T06:47:40.068460Z",
     "iopub.status.idle": "2023-11-07T06:50:13.475114Z",
     "shell.execute_reply": "2023-11-07T06:50:13.474567Z",
     "shell.execute_reply.started": "2023-11-07T06:47:40.068611Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5 --------------------------------------------------------------------------------------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/woody/anaconda3/lib/python3.8/site-packages/lightgbm/engine.py:181: UserWarning: 'early_stopping_rounds' argument is deprecated and will be removed in a future release of LightGBM. Pass 'early_stopping()' callback via 'callbacks' argument instead.\n",
      "  _log_warning(\"'early_stopping_rounds' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
      "/home/woody/anaconda3/lib/python3.8/site-packages/lightgbm/engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
      "  _log_warning(\"'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training until validation scores don't improve for 800 rounds\n",
      "[100]\ttrain's auc: 0.955841\ttrain's binary_logloss: 0.103287\ttrain's binary_error: 0.0331309\tvalid's auc: 0.920514\tvalid's binary_logloss: 0.122003\tvalid's binary_error: 0.0377868\n",
      "[200]\ttrain's auc: 0.97501\ttrain's binary_logloss: 0.0833686\ttrain's binary_error: 0.0285425\tvalid's auc: 0.934009\tvalid's binary_logloss: 0.112875\tvalid's binary_error: 0.0377868\n",
      "[300]\ttrain's auc: 0.985191\ttrain's binary_logloss: 0.0710984\ttrain's binary_error: 0.0259447\tvalid's auc: 0.938972\tvalid's binary_logloss: 0.110014\tvalid's binary_error: 0.0369771\n",
      "[400]\ttrain's auc: 0.99133\ttrain's binary_logloss: 0.061986\ttrain's binary_error: 0.0239204\tvalid's auc: 0.940783\tvalid's binary_logloss: 0.108899\tvalid's binary_error: 0.0369771\n",
      "[500]\ttrain's auc: 0.994708\ttrain's binary_logloss: 0.0547304\ttrain's binary_error: 0.0212213\tvalid's auc: 0.941872\tvalid's binary_logloss: 0.108021\tvalid's binary_error: 0.0367072\n",
      "[600]\ttrain's auc: 0.996725\ttrain's binary_logloss: 0.0487103\ttrain's binary_error: 0.0188596\tvalid's auc: 0.94235\tvalid's binary_logloss: 0.107712\tvalid's binary_error: 0.0365722\n",
      "[700]\ttrain's auc: 0.997955\ttrain's binary_logloss: 0.0435959\ttrain's binary_error: 0.0164305\tvalid's auc: 0.943288\tvalid's binary_logloss: 0.107154\tvalid's binary_error: 0.0361673\n",
      "[800]\ttrain's auc: 0.998802\ttrain's binary_logloss: 0.0391322\ttrain's binary_error: 0.0134615\tvalid's auc: 0.943329\tvalid's binary_logloss: 0.107203\tvalid's binary_error: 0.0358974\n",
      "[900]\ttrain's auc: 0.999311\ttrain's binary_logloss: 0.0352163\ttrain's binary_error: 0.0108637\tvalid's auc: 0.944282\tvalid's binary_logloss: 0.106884\tvalid's binary_error: 0.0363023\n",
      "[1000]\ttrain's auc: 0.99963\ttrain's binary_logloss: 0.0318323\ttrain's binary_error: 0.0085695\tvalid's auc: 0.944263\tvalid's binary_logloss: 0.107234\tvalid's binary_error: 0.0360324\n",
      "Did not meet early stopping. Best iteration is:\n",
      "[1000]\ttrain's auc: 0.99963\ttrain's binary_logloss: 0.0318323\ttrain's binary_error: 0.0085695\tvalid's auc: 0.944263\tvalid's binary_logloss: 0.107234\tvalid's binary_error: 0.0360324\n",
      "dict_keys(['train', 'valid'])\n",
      "3\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABIcAAAE/CAYAAADc0KMkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nOzdeXxcdb3/8ddnJpN939p0TWlLoRRaoGyyC0ILyiL3IjsiWtwuLuAVXC/3uqCiIj9FBFRUBESQRSiyL7K3QFtatu403ZKmTZp9mfn+/vieNNM2aZM2ySSZ9/PxmMc5c9bPmcKck898v5+vOecQEREREREREZHkFEp0ACIiIiIiIiIikjhKDomIiIiIiIiIJDElh0REREREREREkpiSQyIiIiIiIiIiSUzJIRERERERERGRJKbkkIiIiIiIiIhIElNySERERIYUM1tlZid3sfxYM3s/ETGJiMjw0d19ppfHuMPMftBXMYn0t5REByAiIiLSF5xz/wamJDoOERERkaFGLYdEREREdsG8fn9mMrOdfrTrallvjyEiIiKyO0oOSVIzs2vMbLmZ1ZnZO2Z2drD8f8zszrjtys3MdTx0m1mhmf3RzNaZ2RYzezBR1yAikqQOC763twTfx+lmdoKZVXRsEHQLuNrMFplZrZn9zczSg3UFZvaImVUFx3jEzMbE7fucmf3QzF4CGoGrzOyN+ADM7Krdff+bWZqZ3WBmH5rZRjO7xcwygnUnmFmFmX3TzDYAfwzuP/eZ2Z1mthX4tJmNMrOHzWyzmS0zs8/FHX+n7ff+oxUREdj2HX5j8My/LphPi1v/32a2Plj32eDvhUndHOtzwXf45uA7fVSw3Mzsl2ZWGdyrFpnZtGDdacG9rs7M1prZ1QNz5ZKMlBySZLccOBbIA64D7jSzsh7s9xcgEzgAKAV+2W8RiohIVy4ETgUmAvsC3+lmu3OBWcAE4CA6kych4I/AeGAc0AT8eod9LwbmADnATcAEM9s/bv1F+PvBrvwkiG8GMAkYDXwvbv1IoDCIY06w7EzgPiAf+CtwN1ABjAL+A/iRmZ0Ud4wdtxcRkb7xbeBI/Hf4dOBwgvuNmc0Cvg6cjP9+P767g5jZR4Ef4+9JZcBq4J5g9SnAcfh7RT7wKaA6WPd74ArnXA4wDXim7y5NZHtKDklSc8793Tm3zjkXc879DViK/9LvVpA8mg183jm3xTnX5px7fiDiFRGRbX7tnFvjnNsM/BA4v5vtbgq+5zcD/8Q/4OOcq3bO3e+ca3TO1QXH2PHB/g7n3BLnXLtzrgX4Gz4hhJkdAJQDj3QXoJkZ8Dnga865zcF5fgScF7dZDPi+c67FOdcULHvFOfegcy4GFAPHAN90zjU75xYAt+MTV+y4fdwxRERk710I/K9zrtI5V4X/Mbnj+/dc4I/BfaIxWLer4/zBOfdmcD+5FjjKzMqBNvyPEPsB5px71zm3PtivDZhqZrnB3x1v9vUFinRQckiSmpldYmYLzKzGzGrwGfni3ew2FtjsnNvS/xGKiEg31sTNr8a3qunKhrj5RiAbwMwyzex3ZrY66I71ApBvZuFuzgHwJ+CCIOlzMXBv8JDfnRJ8K9M34u4z/wqWd6hyzjXv4tpG4e85dXHLVuNbIHUXp4iI9I1R+O/cDvH3m1Fs//27q+/i7Y7jnKvHtw4a7Zx7Bt9y9TfARjO71cxyg03PAU4DVpvZ82Z21N5cjMiuKDkkScvMxgO3AV8Gipxz+cBiwIAG/AN9h5Fx82uAQjPLH6hYRURkJ2Pj5scB63q5/1X4kc2OcM7l4pv0g78HdHDxOzjnXgVa8d2RL2D3Xco24burHeCcyw9eec657O7O0cWydfh7Tk7csnHA2t0cQ0RE9t46fLffDvH3m/XAmLh18felXR7HzLKAIoLvcufcTc65Q/ElK/YFvhEsn+ecOxNfxuJB4N69uRiRXVFySJJZFv6BugrAzC7DtxwCWAAcZ2bjzCwP3/QTgKCZ52PAzUFB04iZHYeIiAykL5nZGDMrBL6F7/LVGzn4xE1NcIzv93C/P+N/4W13zr24qw2DbmG3Ab80s1IAMxttZqf2NEjn3BrgZeDHQdHtg4DLUW0hEZGBcDfwHTMrMbNifM24jkFr7gUuM7P9zSyT7evJ7eiuYNsZQUHrHwGvOedWmdlhZnaEmUXwP1A3A1EzSzWzC80szznXBmwFov10nSJKDknycs69A/wceAXYCBwIvBSsexL/h8Yi4A12rilxMb4P8HtAJfDVgYlaREQCdwFPACuC1w96uf+NQAa+dc+r+O5ePfEX/A8Ju2s11OGbwDLg1aD72lP4Fku9cT6+vtE64AF8jaIne3kMERHpvR8A8/F/E7wNvBkswzn3GH6wgmfx3/OvBPvs1N3YOfc08F3gfnyLo4l01p/Lxf+QsAXf9awauCFYdzGwKrh/fJ6g7p1IfzDn1BJZREREpCeCYegrgUOcc0sTHY+IiAwOwWiWi4E051x7ouMR6S21HBIRERHpuS8A85QYEhERMzs76P5VAPwE+KcSQzJUKTkkIiIi0gNmtgr4Cr6YdfzyJWZW38XrwoQEKiIiA+UKfP3S5fh6QF9IbDgie07dykREREREREREkphaDomIiIiIiIiIJDElh0REREREREREklhKogPoSnFxsSsvL090GCIig84bb7yxyTlXkug4Ekn3CBGR7uk+ofuEiMiudHefGJTJofLycubPn5/oMEREBh0zW53oGBJN9wgRke7pPqH7hIjIrnR3n1C3MhERERERERGRJKbkkIiIiIiIiIhIElNySEREREREREQkiQ3KmkNdaWtro6Kigubm5kSH0q/S09MZM2YMkUgk0aGIiIiIDCnJ8rwIemYUEdkTuk90b8gkhyoqKsjJyaG8vBwzS3Q4/cI5R3V1NRUVFUyYMCHR4YiIiIgMKcnwvAh6ZhQR2VO6T3RvyHQra25upqioaFj/A5oZRUVFSZHFFBEREelryfC8CHpmFBHZU7pPdG+3ySEz+4OZVZrZ4m7Wm5ndZGbLzGyRmR0St26Wmb0frLumx1F1H8veHmLQS4ZrFBEREekvyfIslSzXKSLS15Ll+7O319mTlkN3ALN2sX42MDl4zQF+GwQSBn4TrJ8KnG9mU3sV3SBSU1PDzTff3Ov9TjvtNGpqavohIhEREREZbPTMKCIi3RnM94jdJoeccy8Am3exyZnAn533KpBvZmXA4cAy59wK51wrcE+w7ZDU3T9iNBrd5X5z584lPz+/v8ISERERkUEkmZ8Zd9drwMwuDHoaLDKzl81s+u72NbNCM3vSzJYG04KBuh4Rkb42mO8RfVGQejSwJu59RbCsq+VH9MH5EuKaa65h+fLlzJgxg0gkQnZ2NmVlZSxYsIB33nmHs846izVr1tDc3MxXvvIV5syZA0B5eTnz58+nvr6e2bNnc8wxx/Dyyy8zevRoHnroITIyMhJ8ZSLJwzlHNOaIOkcsBtHgfWzbMkd7LFjm4qdst6xj2x2Ptf0y13n84Bgdxy8vyuQjk4oT/XEknTVLF7J+4dO73c5SUjng5IvJzM4bgKhEZLhJ1mfGuF4DH8M/988zs4edc+/EbbYSON45t8XMZgO3AkfsZt9rgKedc9cHSaNrgG/2y0WsWwDr3oSZn+mXw4uIDOZ7RF8kh7rqyOZ2sbzrg5jNwXdLY9y4cX0QVt+6/vrrWbx4MQsWLOC5557j9NNPZ/Hixdsqf//hD3+gsLCQpqYmDjvsMM455xyKioq2O8bSpUu5++67ue222zj33HO5//77ueiiixJxOSJ7LBZztEZjtEZjtLV3TB2t0Sit7X5dWzRGa7Cutb3zfedyt9PylvYY7bEY7VFHW9TFzcdoj/lpWzRYFnO0b5v30/ZojLYgOdMejRFz7JSocd1+Aw2ss2aMUnIoATYs+TeHL76uR9vOAw4768v9G5CIDEtJ/My4rdcAgJl19BrYlhxyzr0ct/2rwJge7HsmcEKw3Z+A5+iv5NDSJ+HZH8DBF0O4Z0M/i4j0xmC+R/RFcqgCGBv3fgywDkjtZnmXnHO34n89YObMmbv8E+66fy7hnXVb9zTeLk0dlcv3P3FAj7c//PDDtxsS7qabbuKBBx4AYM2aNSxdunSnf8QJEyYwY8YMAA499FBWrVq194GLxHHO0dQWpbE1SlOrnza2tgdTP9+x3G/XTkNLlOY2/95PYzS3Rmlu98fw0xgtwTbtsb7NsIQMUlNCRML+lRIyPw3bDvMhImH/PiPVgu2NlHCISCiYho1wyG8bMiMcglDICJtfHgqmnfN0sSx4deyzbb5z266PGbe+q2OaEQpBSihERmq4Tz9D6ZkDTr6YysNO3+U2DTWVTPj7KcSa6wYoKhHpL4PheRGS6pmxt70GLgce68G+I5xz6wGcc+vNrLSrg/XFD80Lq9qZDriWOiyzcI+OISJDx2C4Twyme0RfJIceBr4cZPiPAGqDL+4qYLKZTQDWAucBF/TB+QaFrKysbfPPPfccTz31FK+88gqZmZmccMIJXQ4Zl5aWtm0+HA7T1NQ0ILHK4BWLObY2t1Hb1MbWpnZqm/x8XXPbdgmcjmRPQ2uUprhkT1NrlMY2n/BpaPHb90Y4ZGRGwqSnhslMDZOe4uczIiEKs1LJyA+THul4hUiPhEkLEjkd09S4aWrYOt+HQ0RS/DQ1bhrZ4X04lByjBUjiZWbn7barWEOO78sda2sZiJBEJAkk0TNjj3sNmNmJ+OTQMb3dtzu9+aG5O9WtqQC0Nm4lTckhERkAg+kesdvkkJndjW/KWWxmFcD3gQiAc+4WYC5wGrAMaAQuC9a1m9mXgceBMPAH59ySvgi6t7/Y9IWcnBzq6rr+Jbm2tpaCggIyMzN57733ePXVVwc4Okm0WMxR19yZ3KlpavXTxjZqGlvZ0tjGlsZWanaY1ja17barU8ggKzWFjCCBk5GaQmZqmJz0FEpz0shKC9ZFtl/vXynBsvC2ZRmpKWQFy1LDoaQZylGkJ9LSMwFw7TvfiEVkaEnE8yIk9TNjd70JtmNmBwG3A7Odc9U92HejmZUFPz6XAZV9HnmH9BwAWhpqSFPvb5FhT3mF7e02OeScO3836x3wpW7WzcUnj4a8oqIijj76aKZNm0ZGRgYjRozYtm7WrFnccsstHHTQQUyZMoUjjzwygZHK3orFHJsbW9m4tZnq+lY2N7RS3dDKloZWapp8YqcjCdSRANravOskT2ZqmILMVPIzIxRkpjI6P4OCzFQKMiPkZaaSlxEhLyNCbnoKeZkRctIjZEZ8AictRQkckYGSEkkl6gyUHBKRPZTEz4zz2E2vATMbB/wDuNg590EP930YuBS4Ppg+1F8XEErzyaHWxr7tZiIi0mEw3yP6oltZ0rjrrru6XJ6WlsZjjz3W5bqO/n/FxcUsXrx42/Krr766z+OTXXPOsbmhlcq6FjZubaayroWqupZtyzbUNrG+tpnKrS20RmM77R8yyI9L5BRkpjKhOIu8jAj5GRFyMyLbrc/PjGybT4+oxozIkGBGC6nQrm5lIrLnkvGZsbteA2b2+WD9LcD3gCLg5uCHr3bn3Mzd9Di4HrjXzC4HPgT+s7+uIZSRCyg5JCL9a7DeI5QckmGjoaWd9bXNbNzazLqaJtbVNLO+tom1NT7ps2ZzIy3tOyd9slLDFOekUZaXzszxBYzMy6AsL50RuWkUZadRmJVKUVYquekRQqqPIzLstVkEi7YmOgwRkSGnq14DQVKoY/6zwGd7um+wvBo4qW8j7VpK0K2svbF2IE4nIjKoKDkkQ4ZzjuqGVpZX1rOsqp7llQ2s2FTPuiD5U9fcvtM+xdlpjM5PZ1JJNidOKaEsL4OReemU5qQxIjedkpw0teoRke20EsGiajkkIpJsIhl+0IKoRqwUkSSk5JAMOtGYY+2WJpZV1bG8soFlHcmgqnpqGtu2bZcRCTOhOIvyoiyO2qcorsVPOmV56ZTlp5OWosSPiPROm6USUnJIRCTpRLKC5FCTupWJSPJRckgSpqU9yoqqIPlT6ZM/yyrrWbmpYbvuX0VZqUwszea0A8uYWJLNpNJsJpZkMSovQ928RKTPKTkkIpKcUjN9zSGnlkMikoSUHJIBEY05llbW8ebqGhas2cKiilqWVtYTjfkhvsxgbEEmk0qzOXZycZAA8q+CrNQERy8iyaQtlEZKVKOViYgkm8z0NJpcKq5VySERST5KDkm/2NrcxuK1tbz1YQ2vrqjmrQ9rqG/xNYEKs1I5cHQeJ+1fyr4jcphcmsM+JVmq/SMyhJnZLOBX+FFmbnfOXb/DegvWnwY0Ap92zr0ZrFsF1AFRgpFrBjD0nbSGMojEGhMZgoiIJEBGaph60rGW+kSHIiIy4JQc6qGamhruuusuvvjFL/Z63xtvvJE5c+aQmZnZD5ENDi3tUd5YvYWXlm3ixWXVvF1RQ9AoiCkjcjj74NEcMj6fQ8YVMK4wk2D4UhEZBswsDPwG+BhQAcwzs4edc+/EbTYbmBy8jgB+G0w7nOic2zRAIe9SSyiTnPbqRIchIkOUnhmHrozUMJtdBtaq5JCI9I/BfI8I9ctRh6GamhpuvvnmPdr3xhtvpLFxeP0K3dwW5c0Pt3DHSyu57I+vM/26J7jgtte45fkVpISML390MndcdhhvfOdkHv/acfzfWdM4++AxjC/KUmJIZPg5HFjmnFvhnGsF7gHO3GGbM4E/O+9VIN/MygY60J5oC2eQHmtKdBgiMkTpmXHoyoiEqSeDcJuSQyLSPwbzPUIth3rommuuYfny5cyYMYOPfexjlJaWcu+999LS0sLZZ5/NddddR0NDA+eeey4VFRVEo1G++93vsnHjRtatW8eJJ55IcXExzz77bKIvZY9VbGnkxaWbePTt9by6opq2qG8aVF6Uybkzx3Ls5BKO3KeQnPRIgiMVkQE2GlgT976C7VsFdbfNaGA94IAnzMwBv3PO3dqPse5WWziLdKfkkIjsGT0zDl2RcIhG0slta0h0KCIyTA3me4SSQz10/fXXs3jxYhYsWMATTzzBfffdx+uvv45zjjPOOIMXXniBqqoqRo0axaOPPgpAbW0teXl5/OIXv+DZZ5+luLg4wVfRO5Vbm3llRTWvrdzM/FWb+WCj/xVlTEEGlx09gUPHFzC1LJexhWr6LJLkumoO6HqxzdHOuXVmVgo8aWbvOede2G5nsznAHIBx48btbby71J6SSYaSQyKyh5LxmXE4aQplktKu5JCI9I/BfI8Ymsmhx66BDW/37TFHHgizr9/9dsATTzzBE088wcEHHwxAfX09S5cu5dhjj+Xqq6/mm9/8Jh//+Mc59thj+zbGftbcFmX+qi28trKa5z+oYlFFLQA5aSnMGJfPuTPHcvy+JUwqzVbXMBGJVwGMjXs/BljX022ccx3TSjN7AN9NbbvkUNCa6FaAmTNn7ph46lPtkRyyaIJoG4TVElJkyErw8yIM32fG4aw5lEVqdGOiwxCRgaC8wnaGZnIowZxzXHvttVxxxRU7rXvjjTeYO3cu1157Laeccgrf+973EhBhz62raeKZ9yp59r1KXlq+iea2GOGQceDoPL5x6hSOnVzMAaPyCIeUDBKRbs0DJpvZBGAtcB5wwQ7bPAx82czuwXc5q3XOrTezLCDknKsL5k8B/ncAY99JU1qJn6mvhLzRiQxFRIa44fTMmCxaw1mkRdVySET632C7RwzN5FAvfrHpKzk5OdTV1QFw6qmn8t3vfpcLL7yQ7Oxs1q5dSyQSob29ncLCQi666CKys7O54447ttt3sDQRjsUczy+t4o6XVvH8B1UAjC3M4FMzx3L8lBIOn1BEdtrQ/E9DRAaec67dzL4MPI4fyv4PzrklZvb5YP0twFz8MPbL8EPZXxbsPgJ4IGiNmALc5Zz71wBfwnZaMnxyyNVtwJQcEhm6EvC8CMPrmTEZtUTyyGisA+dALeVFhjflFbajDEAPFRUVcfTRRzNt2jRmz57NBRdcwFFHHQVAdnY2d955J8uWLeMb3/gGoVCISCTCb3/7WwDmzJnD7NmzKSsrS1hxwVjM8f7GOl5atom/vvYhKzc1UJKTxldOmswnppcxsURdxURkzznn5uITQPHLbombd8CXuthvBTC93wPshVCWTw611G4kfUyCgxGRIWeoPzMmu9bUfFIao9CyFdLzEh2OiAwzg/keYf55fXCZOXOmmz9//nbL3n33Xfbff/8ERTSw+vJaP6xu5P43K/jbvDVs2NoMwMHj8vn0R8qZPa2M1JRQn5xHRAaGmb3hnJuZ6DgSqat7RF/617PPMuv5s9g063cUH3lev51HRPpeMj0vQtfXq/vE3t0n/vzbH3HJxp/AlW9B4T59HJmIJJruE93fJ9RyaBhqj8Z46t1K7nr9Q15cWoUDjplUzFWn7MsRE4oYV6TRxUREupOdnQtAY8PWBEciIiIDLZZe4Gcat0BhYmMRERlISg4NI5sbWrn79Q/566urWVfbTFleOl86cRIXHDGOsryMRIcnIjIk5OT6bgTNjUoOiYgkncwgI9S0ObFxiIgMMCWHhoEPqxv54dx3eOa9StqijqMnFfH9Mw7gpP1KSQmr25iISG9kBS2H2ps0Wo2ISLIJZRYB0F6/SX8oiUhSGVLfec65YV80uTc1oCq2NHLfGxXc/Nxy0sIhLj2qnE8dNpbJI3L6MUIRkeEtOyuHmDOiLfWJDkVE9kAyPC9C754ZpefCWT451Fqn5JDIcKX7RNeGzHdeeno61dXVFBUVDdt/SOcc1dXVpKen73K7J5Zs4DfPLmNhRS0Asw4YyXc+vj9jClRLSERkb+VkRGgkjViLWg6JDDXJ8LwIPX9mlN5LzSkg6oy2uk2JDkVE+oHuE90bMsmhMWPGUFFRQVVVVaJD6Vfp6emMGdP12MnNbVFufGoptzy/nPFFmXz7tP05eeoIJhRnDXCUIiLDV2ZqmA1kEm6pSXQoItJLyfK8CLt+ZpQ9l5ORTi1Z0FCd6FBEpB/oPtG9IZMcikQiTJgwIdFhJMzLyzfx7QcWs3JTA6cfVMZPzjmI7LQh888nIjJkmBmbrJCM5uH/0CAy3CT786Lsvdz0FLa4HPIbVJBaZDjSfaJ7yi4Mcs1tUb730GLunV/B+KJM7rz8CI6ZXJzosEREhrUtoUKKWtWlQEQk2WSnp1BDNvkarUxEkoySQ4NUWzTG3+dX8LsXlrO6upErjt+Hr528L+mRcKJDExEZ9qojZRzRuhBiUQjpe1dEJFnkpEdY5nKY1KxuZSKSXJQcGoSeemcjP5z7Lis3NTB9TB7XXnQos6aNTHRYIiJJY236ZNJaHoLqZVAyJdHhiIjIAMlJT6HK5ZHavCrRoYiIDCglhwYR5xzfe2gJf3l1NfuOyOb2S2Zy0v6lw7qKuojIYFSbOR5qgc0rlRwSEUki2WkpVFFAeusWiLZDWH8uiUhy0LfdILFgTQ0/nvsur63czKVHjefbp08lNSWU6LBERJJSc/ZYP7NlVULjEBGRgZUeCbM5VIDhoKESckclOiQRkQGh5FCCVW5t5pdPLeXe+WvIy4jw/U9M5dMfKVdrIRGRBLKsYlpJIbVufaJDERGRAdaUWgLtQN0GJYdEJGmoaUqC1Le0c8Pj73P8z57jvjfWcPGR43n26hO47OgJSgyJiCRYTkaEapeLa6hMdCgiIkOGmc0ys/fNbJmZXdPF+v3M7BUzazGzq+OWTzGzBXGvrWb21WDd/5jZ2rh1p/X3dbRmlPiZ+o39fSoRkUFDLYcSYMm6Wq66dyHvbajjjOmjuPqUKYwrykx0WCIiEshJj7DJ5VJaV4XGKhMR2T0zCwO/AT4GVADzzOxh59w7cZttBq4Ezorf1zn3PjAj7jhrgQfiNvmlc+6Gfgx/O7GsUqhDySERSSpKDg0g5xz3v7mWbz3wNqnhEH+87DBOnFKa6LBERGQH2WkpbHJ5xOorlRwSEemZw4FlzrkVAGZ2D3AmsC055JyrBCrN7PRdHOckYLlzbnV/BrsrljMCNgB1Sg6JSPJQcmiANLdFuervC3l00XqO3KeQmy88lMKs1ESHJSIiXcjNiFBNHjQsS3QoIiJDxWhgTdz7CuCIPTjOecDdOyz7spldAswHrnLObdmzEHsmNyuLLeRQUL+hP08jIjKoqObQAFhd3cAnb36ZRxet5xunTuHOy49QYkhEZBArzk5lk8sj3LgJnEt0OCIiQ0FXRTN79QVqZqnAGcDf4xb/FpiI73a2Hvh5N/vOMbP5Zja/qqqqN6fdSUFWKhtj+bitGpRARJKHWg71o6bWKDc+/QF3vLQKgN9ccAinH1SW2KBERGS3RuVl8LTLJRRrhaYtkFmY6JBERAa7CmBs3PsxwLpeHmM28KZzblt/rvh5M7sNeKSrHZ1ztwK3AsycOXOvsvoFmRHWu0Im1a7VH0sikjT0fddPFlXU8N0HF7OwopYzZ/ii02MLVXRaRGQoGJmXznI32r+pfAfKj0lsQCIig988YLKZTcAXlD4PuKCXxzifHbqUmVmZc66jCc/ZwOK9DXR3CrNSWe+KsK0L+/tUIiKDhpJDfWzlpgZ++q/3eGzxBgqzUrnlokOYNU2thUREhpL0SJiq9AkQAzYtVXJIRGQ3nHPtZvZl4HEgDPzBObfEzD4frL/FzEbi6wblArFguPqpzrmtZpaJH+nsih0O/VMzm4Hvoraqi/V9Lj8zlaWuiHBTNbQ1QSSjv08pIpJwSg71oafe2ciX7nqTlJDxlZMm89ljJ5CTHkl0WCIisgdS80f4QZcbqxMdiojIkOCcmwvM3WHZLXHzG/DdzbratxEo6mL5xX0c5m4VZqayzgWhbF0HRRMHOgQRkQGn5FAfaGqN8tfXVnPDE++z38gcbrtkJqW56YkOS0RE9kJRXh6NmzPIbNyc6FBERGQAFWRFWN+Rp6qtUHJIRJKCkkN7qbktymfumMcrK6o5ap8ibjr/YEpy0hIdloiI7KVR+elsJpvMxk2JDkVERAZQcXZaZ8uh2orEBiMiMkCUHNoLzjmuunchr6yo5uf/OZ1zDu2ylayIiAxBZXkZrImWUFa1lHCigxERkQGTHglTn1bq32xdmz0D9noAACAASURBVNhgREQGSCjRAQxlNz+3nEffXs81s/dTYkhEZJgZlZ/OO248VvVOokMREZEBlp+Tw9ZwgVoOiUjSUHJoD726opqfP/E+x04uZs6x+yQ6HBER6WNleRlscnmE2pv9aDUiIpI0SnLSqLJiJYdEJGn0KDlkZrPM7H0zW2Zm13SxvsDMHjCzRWb2uplNi1u3yszeNrMFZja/L4NPlMVra7nsj/OYUJzFr847mFDIEh2SiIj0sVH56dSS5d801SQ2GBERGVClOemsdUXqViYiSWO3ySEzCwO/AWYDU4HzzWzqDpt9C1jgnDsIuAT41Q7rT3TOzXDOzeyDmBOmtT3GI4vWcdW9C0lNCXHPnKMozEpNdFgiItIPyvIyaLBs/6ZZySERkWRSmpPGirYiXM2H4FyiwxER6Xc9KUh9OLDMObcCwMzuAc4E4oswTAV+DOCce8/Mys1shHNuY18HnChNrVFO/3//ZkVVAyNy07jhP6drVDIRkWEsHDLScgqgCbUcEhFJMiU5aSyLlmJtjVC3AXLLEh2SiEi/6km3stHAmrj3FcGyeAuBTwKY2eHAeKCjQrMDnjCzN8xszt6FmxjNbVG+//BiVlQ1cN0ZB/DyNSfxsakjEh2WiIj0s4yCUX6mfkNiAxERkQFVmpvGahc8729enthgREQGQE+SQ10V1NmxbeX1QIGZLQD+C3gLaA/WHe2cOwTfLe1LZnZclycxm2Nm881sflVVVc+iHyBX3v0W986v4Irj9uGSo8YTVo0hEZGkkFHqBxxwm1clNhARERlQpTnprHQj/ZvNKxIbjIjIAOhJcqgCGBv3fgywLn4D59xW59xlzrkZ+JpDJcDKYN26YFoJPIDvprYT59ytzrmZzrmZJSUlvb6Q/rJmcyNPvruRL54wkWtP2x8zJYZERJLFqBElrHOFtK58OdGhiIjIACrJSWO9KyJmESWHRCQp9CQ5NA+YbGYTzCwVOA94OH4DM8sP1gF8FnjBObfVzLLMLCfYJgs4BVjcd+H3L+cc331oMRmRMBceOT7R4YiIyAAbX5TFk9FDSfnwRYhFEx2OiIgMkNKcNKKE2ZoxCqrVrUxEhr/dJoecc+3Al4HHgXeBe51zS8zs82b2+WCz/YElZvYevvvYV4LlI4AXzWwh8DrwqHPuX319Ef3l3vlreO79Kr5x6hRG52ckOhwRERlg5UWZvO32IdzeqF+ORUSSSF5GhNRwiKrUMbBpaaLDERHpdz0ZrQzn3Fxg7g7LbombfwWY3MV+K4DpexljQixeW8t3HlzM4RMKueSo8kSHIyIiCTA6P4PVBEWpX/oVnPnrxAYkIiIDwswozU1jVbicydWvQXsLpGikYhEZvnqUHEo2bdEY37hvEUVZadx84SEqQC3SX2IxiLaAhaG1HmLtEG3z023zwftoe9x8G7iY7+bT3gRtzZ3T5hqor4TWBsD57dpbgldz19OOGMIRCKdCSnrnA2DHeVy0c95CnduEwv5c2+KNBvG3+e0s7N+7aLAuCtPOhjP+X0I/eumZlHCI9rzx0Ai89Rclh0REksio/AwWN47lY7F2qHofyg5KdEgiIv1GyaEu/PqZZby7fiu3XHQIxdn6hUD2kAsG9euLIuatDdC42Scz2po6p21NOydH2psA84mJljpo2gLRVrYNMuicXx5tg7YGaG2EtkZ/jrZGf8yO5IuL+f3S8yGr2Cc6/EGCZEirT9pEW31iJTUb0nJ8gmXHuGJtfjkEy5t9UqbPGWQW+jg6PoeUtCCZkw6pWZBZFLcszcflov66o62diSPw1xwKkjyhsD/etoRTs/8cskf46w+lbP/q+AxD4WD/FD8/6uB+uG7pL7lFo6hvyiLbNUBzLaTnJTokEREZAGPyM5hXHbQe3bhEySERGdaUHNrBms2N3PrCCk4/sIxZ08oSHY7sKef8K9RFWS3nfMImFoNNH0Ddup1bpbQ3+z8CO5IlzVuhsdr/oW8WJGYafZKjtcG3emlr7Gz10pFkwEFark8IAD5ZYdBS75MSkQwfT3NNsDpIQlgoSEgE8Tdt2bPPwUI+sZOSHrwPElWp2T4hkpoJ6bmQM9InTSKZ/hVO6Yyj4/wd19+ho5VNKMXPR9v859BS5689PQ8i6ZCS4aehiP+MnfPXnZLm14UjvjVNem7nsUIpfvtwMI1f3jHtSNqkZHR+linp/vMO66tN+s6Usly+svK/+H3K9bDhbSg/JtEhiYjIABiVn8Ej9YW4zHRs45AZU0dEZI/oL6g4sZjjxqeW0tQW5ZrZ+yU6nKHPuc5WE/FiMZ9ACUe6b1UTbYeta6Fug0/ebF0PdR2vDb7bEPiEQGqWTw50JHUaN0NDFbRshdQc3+Il2uqTPR0tPVLS/bL4ZEd3LOxboWQWBq1BguRGxyu7NIgjO0hcBEmNcKpPrjTXBi1IHNta70Qy/DW2N/nl6XmdLVLiX7GgK1PuKH+ejkRLJKNzvqsp+JYwKRldJ8hEpMemj8nn/vbx/o65boGSQyIiSWJ0QQatsRBtRVNIVXJIRIY5JYcCzW1RvnzXmzz1biWnH1jG2MLMRIc0NETbYf1CWPlckLTZCLUVvkVN/QbfiqR4SpAMafXJleplnS1lIpmQUeiTO631QU2WtqBrU+v25wqnQk6Zf5Xs65MpbU2+W1Tz1qAlTLZvBZNZBFklvlVP3XqfJMkoCFqwpPhWPinpULgPFEwIWsCEO1ulpKT7hE1qll8mIklrbGEG1eTRlDGSjPULEx2OiIgMkI7Rimtzp1Cy9pnO1uciIsOQkkOBP728iqfereSbs/bj88fvk+hwBl4saKnSXOsTN801QVeffJ+saaiChk2+e1HdBt8qpfJdWP2yb6EDPpmSVQp5o30NltGH+GVV73V2M3JRGHcUjJwGmE/SdNTSScvuLAocyfSJm9zRPtmTO8ond3RDFpEBNqbA/1iwMWsK5esXJDgaEREZKKOC5ND6tImUNN7rW67njEhwVCIi/UPJIWB1dQO/fnYZJ04p4QsnTEx0OAOjaYuvnbHqJVj6uO8q0dHlabeCIr8F5TDtk1B+rH/pZikiw1BBZoT9RubwfPMkyuufh+rlUJQk9woRkSTW0XJoebicgwA2vq3nXREZtpQcAn76+PsAXHfGtARH0g+c80Nvrnze1+QBWP40rPy3bxEEMOoQOPpKiGT51j0Z+b7FUCgMTTW+JU9Wse+qlZ7nu3WpBY+IJAkz4+T9R3D3C1O4NAKsfUPJIRGRJJCRGqYwK5XF7WM5G/yIZZNOTnRYIiL9IumTQ8sq63js7fWcd/g4xhUNkzpDsRhUL4W1b8K82/wfMvFyyuCgT8GUWTB6JuRqVDYRkV2ZMjKH30VH4lLD2KYPEh2OiIgMkNH5GSyvj/hSBxuXJDocEZF+k/TJobteW0NKKMRXT5qc6FD2XNUHvmVQ1fu+CPM7D0FlcPPKGwuzfwbjjvDdwGJR1e4REemlcYWZtJGCuSi88DM45mu+YL2IiAxrYwoyeH9jna+XuUEjlonI8JXUyaGFa2q489XVzJo2ktLc9ESH0zOtDX40sMbNfn7x/bDwLr8uNQda6yB7JJx8HexzAow4QKNtiUi/M7NZwK+AMHC7c+76HdZbsP40oBH4tHPuzbj1YWA+sNY59/EBC7yHdhrBsmKe/44VEZFhbXxRFk+9u5HYQQcQWv40tLdCSmqiwxIR6XNJnRz62ePvk5sR4X/PPCDRoeyac7DmdfjgMXj9dp8Aijf+GDjtpz4R1Nbkh2FXyyARGSBBYuc3wMeACmCemT3snHsnbrPZwOTgdQTw22Da4SvAu0DugATdSwWZEbLTUvjTpJu4dNmVvij1PickOiwREelnE4ozaYs6NufsS3GsHTa9DyMPTHRYIiJ9LmmTQ/NWbebFZZu4dvZ+5GcOguz/5hW+RpAZrF/oWwZll/rh5Zc9DRsW+RHC9vs47P8JXyA6lAKFE/0w7x3JoEhGYq9DRJLR4cAy59wKADO7BzgTiE8OnQn82TnngFfNLN/Mypxz681sDHA68EPg6wMce4+YGWMLM3mhbT8ujWTBpqWJDklERAZAeZHvQrwyXE4x+LpDSg6JyDCUlMmhWMzx9XsXkJ8Z4aIjxycukI1L4PXbYPXL/leIbcwnhho2+aTPqEN8N7EZF0J2ScLCFRHpxmhgTdz7CrZvFdTdNqOB9cCNwH8DOf0Y414bV5jBiqoGKJ4EG1V3QkQkGUwo9smh91pLOCyS6X/MnX5egqMSEel7SZkcuvrvC1mzuYkfnX0gWWkJ+AhiMVjyD/jH5yCcBuOPggP/w3dRaG+Gsul+yPjWRsCp6KmIDHZd9WN1PdnGzD4OVDrn3jCzE7o9gdkcYA7AuHHj9jTOvTK+KItn368ietDJhF/8Ocz/A8z8TEJiEREZjHpQf24/4I/AIcC3nXM3xK1bBdQBUaDdOTczWF4I/A0oB1YB5zrntvT3tXQoyUkjMzXMys2tMPpQqHh9oE4tIjKgki45tLamiQcWrOWsGaM477CxiQniwS/AonugZH/41F+guJuR0lIzu14uIjK4VADxX6hjgHU93OY/gDPM7DQgHcg1szudcxfF7+ycuxW4FWDmzJk7Jp4GxAGjcmltj7EmezrlAI98DbJHwH6nJyIcEZFBpYf15zYDVwJndXOYE51zm3ZYdg3wtHPuejO7Jnj/zb6NvntmxviiLFZVN8CYw+Dlm3yNT5VyEJFhJpToAAbS+xvqmH3jCzgHV50yhVBogIs2122Ex77pE0OHXApznu0+MSQiMnTMAyab2QQzSwXOAx7eYZuHgUvMOxKodc6td85d65wb45wrD/Z7ZsfE0GBx8NgCAF6NTYEJx/mF91wADdUJjEpEZNDYVn/OOdcKdNSf28Y5V+mcmwe09eK4ZwJ/Cub/RPeJpX4zoTiTVZsaYOzhEGuHdW8NdAgiIv0uqZJDNz2zlK3N7fzPJ6buPCxxf3rzL/C74+AX+8Nrt8CU02DWj/WLg4gMC865duDLwOP4Ecfudc4tMbPPm9nng83mAiuAZcBtwBcTEuxeGFuYQWFWKm+sbYFL/wmnBb0hnvhOYgMTERkcuqst11MOeMLM3gi6EncY4ZxbDxBMS/c60l4qL8riw82NtI2a6ReseW2gQxAR6XdJ1a1sydpaTpk6gk8fPWFgTrhpGTz7A1jygO9CdtQX4dDLoGjiwJxfRGSAOOfm4hNA8ctuiZt3wJd2c4zngOf6Ibw+YWZMH5PHwooav2DGBTD3G7DwLv/9rtFrRCS59aT+3K4c7ZxbZ2alwJNm9p5z7oUen7wfa9NNKs2mPeZY3ZTOpKJJ8OGrfXp8EZHBIGlaDq3c1MCq6kaO3Keo/08WbYd/fQtuPgKWPgmHXwFXvACn/ECJIRGRIWzG2AKWVtZT19zmBwv4xjIIpcAtx8CzP/YDDoiIJKee1J/rlnNuXTCtBB7Ad1MD2GhmZQDBtLKb/W91zs10zs0sKenb0X33HeEH01y6sR7Kj4VVL/nnfRGRYSRpkkPPvOfvI6ccMKJ/T7T8Wd+F7NXf+KHnr3wLTvsppKT273lFRKTfTR+bh3Pw9tpavyCrGM653c8/fz3cfpIfaXLdW1D5XuICFREZeD2pP9clM8sys5yOeeAUYHGw+mHg0mD+UuChPo26ByaWZGMGH2ys96MLt9bBujcHOgwRkX6VNN3KPthQR3F2KmMK+rHWUEsd3H85pOXAmb+BgwdlTVUREdlDM8bmA/DWhzV8ZGKxX3jA2b710N8u8n8svHozPPN/ft3VyyC7b3/BFhEZjJxz7WbWUX8uDPyho/5csP4WMxsJzAdygZiZfRWYChQDD5gZ+L9P7nLO/Ss49PXAvWZ2OfAh8J8DeV0AGalhxhZk8kFlHXzkOMBgxXO+QLWIyDCRNMmhldUNlBdl9e9JXroJGqvhgr/DmEP791wiIjLg8jNT2acki7c+3LL9iv0/Ad/bArd/tDMxBPDCz3zr0XjOwau/hbfvhc88Dilp/R+4iMgA6EH9uQ347mY72gpM7+aY1cBJfRjmHtl3RDbLNtZDZiGUHQQrnofj/zvRYYmI9Jmk6Va2alMD5cX9lBxqbYS3/gov/BQO+KQSQyIiw9ih4wp4aVk1zW3R7VeEQr62XLzXfwdv39f5vuINuPcSePxa3/Xs5ZtUt0JEZAiYPCKHFZvqaYvGYMLxfsSy1oZEhyUi0meSIjm0rqaJyroWyov6oUvZor/DT/eBh74IxfvCrOv7/hwiIjJonDx1BE1tUf65sIs6q+XHwBdehmsr4BM3+WX3Xw7Vy6HqA9+y6N24EhzP/ADu/OTuT/r2ffDuI31zASIi0muTS7NpizpWVzfApJMh1gbLnk50WCIifWbYdyurb2nnM3fMIzsthVnTyvrmoLEoLP6HH754+TOQNxaO/gocfDFE0vvmHCIiMiidvP8I0iMh3t9Q1/UGIw7w00MvhaJJcMdpsOheqAoKVJ//Nxh3JPxkvH+/8nnfiqhshn/fUgcu6pNJ//gsNNf6F0BWKWSVwOVPQLQVGjf7Lg4v3OAHQsgqhbLpYAZLnwjOdw9Mmb37C3MOtq6D9Dxoa4TmrZA3BqItfpmISBKLH7Fs0tSjIbMYljwAU89IcGQiIn1j2CeHHlm4jvc21PHHyw5jUmn2nh9o6ZO+8Fxrg3+Q37wCcsf4otOn/BAy8vssZhERGbzCIWNiSTZLK+t3v/G4IyF3tB/JDOCwz8KUWX7+C6/AK7+GBX+FW0+As2+FxffD0schnOaTMjuKtkDlEnjux/DmX6Cldvv1WSWw7Mntl919HvzPDtsBrH3TJ5AiGZBZBO8/Bu910zrpi69B6X5+ftHfIWcETDhu99cvIjJMxI9YNvvAMp8UWniPLy+R2o8D3oiIDJBhnxyat2oLJTlpnLDvHo4W01IP/7oG3roTcIDBqIPh3D/Dfp/wNSZERCSpTC7N5pUV1TjnCEbX6VooDBc/AOsX+dY8k07uXDdiKpx2A7z7T2jZCg/M6VwXbYG8cVD74fbH+/gv4b7P+KQSwNFfhZdu9PPT/gNOvwF+Ug6TT4Vjr4InvgMVr8PPJsMF98DzP4PCfaC1Ht78064vMnc0bF3r59e8BiVT4B+fg7f/7pd9ZRH880oYMQ1O/eFuPzMRkaFsuxHLwI9UOf8PPsl+wFmJDU5EpA8M++TQik31TC7N3vXD+648+nVY9DcYfwycfzdYCNL2ogWSiIgMecdOLuHBBet4fMlGZk0bueuNS6b4V1dSM+Hr78Drt8HT122/7quLfCvVh/8Lzr4Fskf4kc3e/5cf6eyAT8LJ/wN1631Xto9c6RNQ397otzPz6+84DRoq4baPdh3DxI/67mRV7/mWTcdfA+EUSM/3y2880CeHlvzDt6Dt8KuD/HTFc1D1PuSP87X3jvz87j4+r/I9+NMnfGxn/86P4NZYDbVr/Prz/+ZbWTkHbU2Ag9S9HFiitcEfo6Uemrb4e3ruKP9ZiYjsxpSROby7fqt/M/5o31pzyQNKDonIsDDsk0MrNzVw+oF7WGuotsInho75mn/AFhERAT4xfRQ3PbOUbz3wNkdPKiInPbLnB0vLgcMu90megz4Fmz7wrXbMoGgiXDZ3++3Puc2/Onzy1u3Xx9e+G3sE7DvLF00tmQIbF/vlEz8KBeVwwrWQXdp9bHmjIafMd30DyBkFV74Jd54Dq1/q3C6+K9vMy3xyastq+PCVzuVl06F0/873S5/wiSGAB67w0zGHdSaHnvk/aK6BhXd3JqVO/PbOQ0fXrIH6SsDBmJlQ+S401cD4o/z6reuDuk4L4LXf+mtf/kzn/rN+0vOElogktQNH5/HUuxupb2knOy0Fpp7pRyzuSDyLiAxhwzo5tLyqnprGNvYp2cOWPquDh9qpZ/ZdUCIiMuSlpoS46byDOfM3L/H7F1fy1ZP33bsDpufBaT/z82Nm7n2AHcIpcMHfOt+veM7/2h3uRTLrI/8F//qmn//PP/oaRZc8BKv+DfucCO0tcNMMn9wC+EGp79L24i/BxTqPUzQZ/mv+9rFkj/A/wKTlQFou7P8J31rpuR/DW3/pTBp1ePaHsOJ5v11mEbzwM9j0fuf6fU7oTCR9a52vo/TEdzpjg87EkIV91755t8MRV/hk3PpFsGGRrycYb80834Lr6K/AjPN3/Xk1b/Vxlk2HGRd0v92Lv/Tbnvht/+8EvgXZ+oW+IHjNh75LYlrOrs8nIgPmwDF5OAdL1tZyxD5FMPUs/x3yweMwrQcjT4qIDGLDOjn06CL/MPiRiUV7doB3HvQjv4w8qA+jEhGR4WD62HxOmFLCPa+v4UsnTiISHgI16PY5off7HHGF/6MnFoXcoCVuOOJb4IBvqXTlAmioghun+WX//rmfnvR9391i3u99naT/yYPUbD+C2vKn4bj/hiO/sP358kbDGf8PjvuGH7UNIBTx3c5e/Y0vyL36xa5jje/29qNRnfOZxfDZJ/1xYm2Qku67gyy6Fx76IlyXD4d9Dub/3ie0Kub5rnLn3A7P/wTe/LM/zoOf9z8YxRefba71ia3mWt8a6oEr/DFCEVj2lE/2HPlF3zqstdGPRjf/9/64AK/9DnJGQrRt5xpTPx4D393kE1mPX+sHxzj8c3DIJfCvazvrRmUW+2Uv/sK/P/0X/nwi0qcOHO1Hbny7Izk0/iOQPdIXplZySESGOHPOJTqGncycOdPNnz9/9xvuxncefJtHF63nre+d0vud35sL95wPMy+Hj/9ir2MREekLZvaGc64Pm5YMPX11j+gLT76zkc/9eT4/PHsaFx4xPtHhJF7tWrj9ZP8H0yn/5+v5AGxZBb+avvP2X33b1yrq7Tl+OdXPT/sPn8Axg9du9d3WRh8Cvz8VNr7duc/omfC5p3c+Vmsj/KiXXc+P/yaMOsTXH1z2tE/ITDsHFv8DP3AF/ppqdkj0lB/rW1t1SM32f0y2NnYuS0n33dhrPoTb42pExRcH76krF0DhhJ2XO+eTVtmlvq5Ugf677bVYzNfhGnfkoKxXpftE/94njr7+GQ4dX8BN5x/sFzx1nR8Y4GtLOr/zREQGse7uE8O65VBVXQvF2Wm933He730h6pL94GPX7X57ERFJSifvX8rk0mzmvr1eySHwrX6uenfn5QXlcMUL8M7D8O8b/LIzft37xFDHOS5+ENJzYfShncuPiBvt7XNP+1HVRh4I0Xa/T1dSM+GzT8OCu3xrnnhjj/A1oEJh3xKobAbcdmJni594i+/3yZ6PXQdjj/Qjwi35h0/EjD4E/jC7MzF06GU+rkknd5+YyS6BTz8Kd5zu329d60dK3f+M7QuXn/JD/6ySP9bXgHrka53rbpoBn3sGNi31saXn+n+HP5/pC513OP3nvk5TONWPfpczouuYhoqV//Ytv1e9BCmpvth5fK2r3lr1kv+3zB/XWfR9/u9h7tVwzu/hwP/o3Patv8K6N2HdW5A3xndDjP9vVIaFA0fn8fba2s4Fh1zsk8QL/upbPIqIDFHDNjkUjTmWVdYzMi999xvHa2uGp//Xdye76H719RcRkW6ZGcfvW8KfX11Nc1uU9Eg40SENXmXT/evIL/guVLl7OFgEwMQTd70+JW3nukHdGTPTvz76HT9aWkG5Ly6blguhHboKzv4ZPLbDH39jDvMtfWZ+xo/21iH+/F9/B+65wCcKTv5+z+IqPwaO+bpP5Jz4LZ9w2roW3rgDDv20L/ydUdC5fckUmH6+Twb97li/rLsR6uI9elUwYz4JNvsnfnS4lHRorYdIlv8cYlFo2QrhtO271SVatN13WVwU1NaqfGf79Tcf6acjpvk/3Cef4utmmcGCu+Hlmzq3Tc3yCbiF97CtFVj88V64wSd9aoKi6fdfDtXL4OCL4b7LfGuiDmvfgHce8vMHnedrY33kSt8dMz0P2pv9K15qTmf9KRm0DhyTx7+WbKC2qY28jIj/f3PCcfDmX+CYq3b+3hARGSKG7R3ooQVrWV7VwBdPmNS7HVe+4EdHueDv/gFARERkF47dt4TbX1zJ6ys3c9y+JYkOZ/DLKk50BF3LLPQvgIz8rrc5Yk5nC6XXb4P6jT6ptDtp2XDpw72PacdEUkE5fHVR99tHMqDsILj8Kfj9yduvyxkFYw713fKmn+9bGtVW+D9sZ/0YHvkqvHaLX/beI537FU/xBcaf/SHUrPbLzr8HIplQt8HHNO6I3l/bjmIxXyy8I2FiId8iastq300xs9AnglY864ugA+B8a6mGKl/jacosKJrkE2cL7vKtxzpsXAx/v9TPTzjeJ9weDEap2/8TfoS7Vf/29aY6loE/3tgj4Ilv++RY0UT/eveffv1zP/avDlNO96P3HXguvPdPX4tq0T1+3X2XBZ/pvn5Uwh1lj/AtuTB/3tL99uYTlX5y0Jig7lBFLcdMDr7PDrnUJwtXvbBntd1ERAaBYZscuuu1DynKSuWTh3TTlLwrj14N84LhgcuP6Z/ARERkWDm8vJDUcIh/L61SciiZHP65REfQvTEz/ah0q1+CT8/1o7VNO2f7+jjx3fAATr7OJzziE0PgW7w8EGxbNgPWL4C7z9t+m6ln+e5tx17lu3J1J9rmR2nLGekLaIPvyl+9HCpe70zM7CijMEhovegLfO9o8qm+PmT8j3oTP+rrUa19wyexUtLhzqBg8Mrn/atj30/d6efffQRq18DEk6AkbgRC53xSc/RMKA5+dKz6ABbfB6EU38rcxfz0wHN9kXaAph/4guOhsG8VhINXb+5MDB17lS+MDr4O1LKn4G9Bi7Njvt7zVmYyoGaMzSdkMG/V5s7k0H4f9zW83vyzkkMiMmQNy+RQY2s781dv4bKjy7GeFgpc9pRPDE0+ZeeRSERERLqRkRpmZnkB/166KdGhiHhmcMnDviV0T1tqFU2ET/0VHv8WfPI2/xyUPdInUZ6+Dk79k/geMwAAIABJREFUMUyZDW/fB4/9tz92Srpv6fPOg/4Yr/0WCib4Okun/MC36ql6v/McNauhaYufH3eU76bV0aomNQdKp8LZtwDma1OteN53nWvY1DliXNkMOOMmvw34WkklU7ovDB1f8+fr7/kR8B7/ti8mPu1sOO2GzvX7f7z7z3P6Dgmxkn3/P3v3HV91df9x/PW5N3sBmQQIO4BMkbAFAUEBtWjdWgfVIip11Vr7a61t1ap1tbhwr6q4KypKEWWo7L33CjMQZJN5fn98LxIwaIAkN7m8n4/H9d77Hfd+rsC9yfue8zne6KOfEl0Let19+LYO13pT/2ISDw+zOg+FnCVegAaHQiOpcuKjwmlRO4Hpa3IPbQyP8gLM6S96o/OO1udMRKQKC8lwaOP33pDkdvWOMiz8SEs/h/d/7f0QdNHL6jMkIiLH5PTMZP75xVK27j5Aavwx9roTqQj+sGOfwnfKuT8OSNpcdHjT5XaXepeSnPMCox1rYcMMb3TRjJe9ffHpULutdzsu0Ox6+Rh4KrBISs36MGTCoSl9B13y+rHVXhYH+1xd8lr5P3ZZHZz6dySzE2ucLZWqU6NE3pm+noKiYsL9gR5DXW/yvmj+brjXu0tEpJoJyXBofa63LGzdWtE/f/CWhfD25d689gsVDImIyLHrmZnCP79YyltT13Fb32Y/f4JIKDGDgY94t/P3eqN8CvO8IKTDtV6D8JKWfOaNngmL9KaXRcRWeskiJ6Jjw0Re/W4NCzfu4tSMwJfRNet7KxzOfM2bMhiXGtwiRUSOUUiGQ4s27QKgee0yBD3LxwIOfv2/E1s5RURETlqt6iTQsWEt3py6jt/2ycTvK+OUZpFQExHrrUj3U1qcUzm1iFSQjg29lQKnr849FA6B1ytqzlsw+Wno97cgVScicnxCcq3FBRt20jAphoSo8J8/eOsiSKinYEhERI6bmXF9j8bk7M5j+LjlwS5HREQqUGpCFA2SYphWsu8QeA3LW13g9R7al1v6ySIiVVRohkMbd9Kqbo2fP7AwD7JnHFp5QkRE5Dj1PSWN2Ag/z05YyYGComCXIyIiFahjw0RmrMmluNgdvqPH7yB/D0wdEZzCRESOU5nCITPrb2ZLzWyFmd1dyv5aZvaRmc0zs2lm1rqs55a3nfsKWJ+7n9Z1fiYcyt8L96dC7kpof1VFlyUiIiHO7zOeuuI08guLefIrjR4SEQllnRolsmNfAcu27j58R+3W3srH3z0JuzYGpzgRkePws+GQmfmBp4EBQEvgcjNrecRh/wfMcc61Ba4G/n0M55artbl7AWiS8jPNDRd/6l23/9Xhq3CIiIgcp17NU+jaOImR09YHuxQREalA3Zt6qwF+u2L7j3f2/RsUFcCEf1ZyVSIix68sI4c6ASucc6ucc/nASGDQEce0BMYBOOeWAA3NLK2M55ar7XvzAUiOj/zpA8f8ESLi4LzhFVmOiIicRMyMs1qlsX1vPt+t3BbsckREpILUrRlNo+RYvl1Rynt9YiPoeB3MfBXWTq702kREjkdZwqG6QMmvQLMD20qaC/wSwMw6AQ2AemU8t1xt3+OFQ0mxET9x0ErYtx1anQ8+f0WWIyIiJ5kLO9QjLjKM616dwY7AFxYiIhJ6ujdNYuqq7eQVltJnrs89UDMD/nuj185CRKSKK0s4VNp6vEd0XuMhoJaZzQF+C8wGCst4rvckZkPMbIaZzcjJySlDWaXL3ZsHQOLRwqGC/TDySoiIhzP+cNzPIyIiUpqEqHD++otW5BUW8eDni4NdjoiIVJA+LVLZm1/ElFWlrEwWGQeDnoEdq+Gbf1V+cSIix6gs4VA2kFHifj3gsO5qzrldzrnBzrlT8XoOpQCry3Juicd43jmX5ZzLSklJOYaXcLjte/KJCPMRFxlW+gFzR0LOYhjwENSsf9zPIyIicjQXdajHpR0zGDV3I7sOFAS7HBERqQDdmiQTHe5n7KLNpR/QqAe0+qXXnPp79aITkaqtLOHQdCDTzBqZWQRwGTCq5AFmVjOwD+B6YKJzbldZzi1v2/fmkxQbgVkpg5bWfgef3gZRNbw3ahERkQpyeaf6HCgo5omxy4JdioiIVICocD89myXz5aKtOFfq5Ajo+1cwH3x8MxQXV2Z5IiLH5GfDIedcITAMGAMsBt51zi00s6FmNjRw2CnAQjNbgrcy2a0/dW75v4xDcvfmH31K2ew3vetffQQRMRVZhoiInOTa1K3BBe3r8sq3a1i7Xf0mRCT0mVl/M1tqZivM7O5S9rcws8lmlmdmd5bYnmFmX5vZYjNbaGa3ltj3VzPbYGZzApeBlfV6yqJfy9ps3nWABRt2lX5ArQZw9gOwegJMf7FyixMROQZHmXt1OOfcaGD0EdtGlLg9Gcgs67kVafPOA6QllLJS2ffrYM5/oNUFUK9DZZUjIiInKTPjt32a8tHsDUxclsNVXWODXZKISIUxMz/wNNAPr7XEdDMb5ZxbVOKwXOAW4PwjTi8Efuecm2Vm8cBMMxtb4twnnHOPVvBLOC59WqTiMxi7aDNt6tUo/aAO18KSz2DsX7ypZqmnVGqNIiJlUZZpZdWGc4412/fSIKmUH8DXTfWuO91QuUWJiMhJq1FyLHVqRHHPxwvJ2Z0X7HJERCpSJ2CFc26Vcy4fGAkMKnmAc26rc246UHDE9k3OuVmB27vxZhxU6ArH5SUxNoKsBon8b9GWox9kBoOehsh4ePcaOHCUUUYiIkEUUuHQ7rxC9uUXUa9W9I93bl/hXdfVqCEREakcZsYfB3rfEHd84Ese+9/So/elEBGp3uoCJbsuZ3McAY+ZNQTaA1NLbB5mZvPM7GUzq3UiRVaEvi1TWbJ5N+tz9x39oPg0uPBFyF0Jb10CeXsqr0ARkTIIqXCooNBr8hYRVsrL2r8DImtA2FH6EYmIiFSA89rVYegZTQB48qsVzM3eGeSKREQqRCmrwXBMabiZxQEfALcFFrcBeBZoApwKbAIeO8q5Q8xshpnNyMnJOZanPWFntawNwBcLjrJq2UGNz4ALX4L10+DtyyD/J8IkEZFKFlLhUFGx9/nj95Xy2XTge4g+yjxgERGRCnT3gBa8eHUWAGMW/swvDyIi1VM2kFHifj1gY1lPNrNwvGDoTefchwe3O+e2OOeKnHPFwAt409d+xDn3vHMuyzmXlZKSclwv4Hg1TI6lbb0ajJpbhpfb6ny44DlY8w2MvAIKDlR8gSIiZRBS4VBhIBwKKy0c2v89RFe5UagiInKS6Nsyje5Nk3h2/Epe+mZ1sMsRESlv04FMM2tkZhHAZcCospxoZga8BCx2zj1+xL70EncvABaUU73l6hft6jB/w05W5pRhuljbi70eRKu+hpGXQ97uii9QRORnhFQ4VPRDOHSUaWVRNSu5IhERkUOuO70RAA98toitu/RtsYiEDudcITAMGIPXUPpd59xCMxtqZkMBzKy2mWUDdwB/NrNsM0sAugNXAX1KWbL+n2Y238zmAb2B2yv7tZXFee3qYAYfzynjYKn2VwYCognwygDYtaliCxQR+RkhFQ4VFHk9h8L8pYwc2rUBEupUckUiIiKH9GmRxrjfnUGxgzenrgt2OSIi5co5N9o518w518Q590Bg2wjn3IjA7c3OuXrOuQTnXM3A7V3OuW+cc+aca+ucOzVwGR045yrnXJvAvl8456pkipKWEEW3JkmMmrOh7AsPtP8VXPEu5K6GF8+EDbMqtkgRkZ8QUuHQUXsOrfjSC4dqNQpCVSIiIoc0SYmjcUosc7O/D3YpIiJSjga1q8ua7fuYdywLD2T2hcGfe7df6gdzR1ZMcSIiPyOkwqFSew45B/+9ybud2TcIVYmIiByuT/NUxi/NYdrq3GCXIiIi5eTs1rWJDPPx7oz1x3Zielu48Vuo3xU+ugFG3wWFeRVTpIjIUYRUOFRqz6HN82DPFjj7QajbIUiViYiIHHLHWc2onRDF796bw859BcEuR0REykGN6HDOa1eH/87ewO4Dx/jeHl0LfvUhdLkZpj0HL/TxlrwXEakkIRUOHew55C/Zc2juSPBHQrvLglSViIjI4WIiwuiRmcz63P2c+9SksvenEBGRKu2qLg3Ym1/Ef2dvOPaTwyKg/z/gsrdhX643zWzUb73bIiIVLKTCoaLSppXt3gQ1MyAmMUhViYiI/NgdZzUjNT6S9bn7WbxJyxiLiISCdhk1aVO3Bm9MWXv8wX+LgTBsGnT7Lcx+E57sALNeh+Li8i1WRKSEkAqHCktrSL1vO8QkBakiERGR0qXXiOb9od0AGD5uOZOW5wS5IhERKQ9XdWnAsi17TqyvXGQ8nHU/DP0GUpp7I4hePhvWTSm/QkVESgitcKiolJ5D+3YoHBIRkSopIzGagW1q88XCzVz10jQa3v0Zb0xeE+yyRETkBJzXrg4JUWH8Z+q6E3+wtJbeambnPws7VnsB0SvnwMqvvIV3RETKSWiFQ4GhlmElew7t3apwSESkgplZfzNbamYrzOzuUvabmQ0P7J9nZqcFtkeZ2TQzm2tmC83sb5VfffCYGU9fcRof3NiNgW1qA3DPxwsZs3BzkCsTEZHjFR3h56IOGXyxYBNbdx848Qc0g1OvgFvneovs5K6CNy6AET1gygjI33fizyEiJ72QCod+1HNo3VRvpbIaGUGsSkQktJmZH3gaGAC0BC43s5ZHHDYAyAxchgDPBrbnAX2cc+2AU4H+ZtalUgqvIsyMDg1q8cyVHVhyX38aJcdywxsz2XWsK92IiEiVcWWX+hQUOd6ZdozL2v+UiFjoehPcOgfOGw4+H3zxB/h3Oxh7L2xdUn7PJSInnZAKh37Uc2hc4Avo+NpBqkhE5KTQCVjhnFvlnMsHRgKDjjhmEPC680wBappZeuD+nsAx4YHLSTtOPircz41nNAHg/Ke+5UBBUZArEhGR49EkJY4emcm8MWUteYXl/F4eFgkdroEbJsLgL6BeFnw3HJ7pDMNPg4+HweSnYcNMKMwv3+cWkZAVWuHQkT2HkjO961OvCFJFIiInhbpAya9GswPbynSMmfnNbA6wFRjrnJtagbVWeRdn1aNTo0RWbdvL6Q9/xRtT1jJjTS6FRVqlRkSkOrmhZxO27s7jw1nHsax9WTXoCpe/DXcshoGPQmIjWPo5jPk/eKEPPJAGz/eGMX+C1ZOgqLDiahGRai0s2AWUpy8CPRp+6Dm0bzuktAB/eBCrEhEJeVbKtiNH/xz1GOdcEXCqmdUEPjKz1s65BYedbDYEbzoa9evXP/GKqzAz490buvLZvE3c/NYs7vnvof8VT13RnnPb1glidSIiUlbdmybRpm4NnpuwkkuyMg5fUbm8xdeGTr/xLgC7t8CaSbB1Eaz5FqY9D5Ofguha0PYyyOwLGV0gMq7iahKRaiWkwqGDDTxrRod7b4jLxkBG5yBXJSIS8rKBks3d6gEbj/UY59z3ZjYe6A8sOGLf88DzAFlZWSfFtLNz2qazc38bsnfso7DY8fzEVQx7azavT17Lq4M7EhMRUh/hIiIhx8y4qVcTbnxzFl8s2Mw5bdMr78nj06DNRYfu5+2BleNgwYcw/QWY+iyY35tpUasRNOkNGZ0gtaU3bU1ETjoh9ZNlz8wUlmzeRVJcJEz+AIryodstwS5LRCTUTQcyzawRsAG4DDhyPu8oYJiZjQQ6Azudc5vMLAUoCARD0UBf4OFKrL1Ku6LzoVFSQ89owu3vzGHCshxa/mUMXRsn8dzVHUiI0uhYEZGq6qxWtWmcHMsz41cwsE1tzCpw9NBPiYyDloO8S94eyJ4Ga76BnKWQswSWfR440KBmhjf7olFPqNcJareBiJjg1C0ilSakwqGComKSYiO8O2P/4l1n9gteQSIiJwHnXKGZDQPGAH7gZefcQjMbGtg/AhgNDARWAPuAwYHT04HXAiue+YB3nXOfVvZrqA4SYyN47dedeH9mNne+N5fJq7Zz2XNTeObK02iYHBvs8kREpBR+nzH0jCbc9cE8Ji3fRs9mKcEuyQuKmvTxLgDOwY7VsGkubF0M21fC5vmw/M/efvNBcjOo2QAS6kCNupBQDxIbQ1orTU0TCREhFw6F+31QXAzFBRAeA8FK50VETiLOudF4AVDJbSNK3HbAzaWcNw9oX+EFhpCLOtTjrFZpPPP1SkZMWEmvR8dzQfu69GuZxsA2lThlQUREymRQ+zo8PnYZz4xfUTXCoSOZeUFPYmNodcGh7bs2wsY5sHE2bFkAO9dD9nTYn3v4+TFJkN7OG2EUX8drip3UFOJSwR/p9X/V72QiVV5IhUOFRc4Lh/J2eRv6/Dm4BYmIiFSAhKhw7jyrGd/vy2fk9PV8NHsDH83ewAtXZ9GvZVqwyxMRkRIiw/z8pmdj7vt0EROX5VTNgKg0CXW8S4uBh28v2O8FRzlLYNsyyF0FG2bB5Ge8L+iPZD6vEXZ0IsQklriudfj9uDRIqOtdh0VUzmsUkR+EVDiUX1RMQkQ4HNjpbYiqEdyCREREKkiY38dDF7bl92c35/Gxy3hz6jp+8/oMbunTlCu7NCAtISrYJYqISMCvutTnte/W8MBni+neNLliVy6raOHRkNTEu3DOoe3Owd5tXli0fTnsy/V6wBbsh/07vBFH+3JhV7Y3bW1/LhTsK/05IuK9PkeR8YFLwuHXEbGQt9sbkRQeDWHREJXgNdmGQPCU5F2HR3nHx9X2botIqUIqHCooKibcZwqHRETkpJEUF8kDF7Thmm4NOeuJiQz/agXPjF9J/9a1ueXMTJqlxQe7RBGRk15kmJ8/DmjBjW/O4u1p6/hVlwbBLqn8mUFcinepX8YVowv2e4HR/lxvteld2bAnB/Zth4K9XvPsvF1eELQ3x7s+sMvbFxHodVSwzwuhyiKqpjdKKaGu97uiz+8FSubzbkfEeaFUWLQXJJV6HbiERR1+HRHr3dYUOqmmQioc+tG0MoVDIiJykmiWFs/Kfwxk+ppc/v3lcj6dt4lP523i190bcc+5pwRvhRwREQGgf+vadG2cxMNfLKFfyzSN8AQvVKlR17vUbnP8j1NcDHk7vdFLzsGB772AaV8uFB6A/D2we7N32Z8L36+D3O1QXASuCFwxFBdC/l7I3weF+4+vDl+YN7IpLDBaKSwa/GFe+HQwWPKFBUIpOxRMmQ/CIg8FTeExXqNvXxgU5nm/3xYXBo71gz/i0HG4QLgVdijkKhl4HbwOi/KewxfuXYdHB8KwOG//wWNL1mQ+8PmO/89FqpWQCocKiooJD/MdGjkUmRDcgkRERCqR32d0aZxEu2tr8trkNXy1ZCsvf7uaj2Zn0yMzhQ4NanFpxwyiwv3BLlVE5KRjZvzjl23o/6+J3PvxQkZc1SHYJYUOX6Cv0UGxSYFpb8fJOS+UKdwPBQcOXRfs//G2g9f5e7xL3m7vuPw93mMUFXjhU+EBb/STK/IevzgQSrlib1thvvdYhXleSIUr8frCvEDIFQcer+j4X9uxOvjcvnAv6AqL9kZXhcd4AVh4DETX9MImf4TXhDws0rtdlO+97oL9Xs3Fgdd6MJSDwDnhXii1P9f7f1mUdygEC4sIBGphXg0+/6GaDoZc4I0gKzjghWpRNQ4PuLDAbQtcjtzuK2W7HWX7kcfb0R/nYNjmO3jt917DwSCyYO+hQPKHvy95gb8LB7z/f8VFh/4uOOe9zsI8aH8lNO5Vrn/UIRUO5WtamYiICNERfoae0YTf9GjM4FenM3FZDqPmbmTU3I0s27Kbm3o3pW7N6GCXKSJy0mmUHMttfZvx8BdL+GLBJvq31iqTVZKZN40sPAqC8XHpnBcaFBceGlFUcgRwcXEglNoPWGDkUyCEKhk6HQxhiou80KHggHdcUUEglNhz6HkOO7fIyyOKC72Aoij/0O2D4VXBPi/U2LfN6zFVcODQsYUHvON9YYdGQx0MR0qGJeDVcrCmmMRAyBTp1VCU7wUlxYWBS9Gh20UlAhQ4NDIqb0/lhmcV5YdQLuzwP/uDUxybnV3uTxlS4dAPS9krHBIREcHvM14b3JE9eYV8Pn8zd30wjzenrmPUnI28O7Qrp6RrhK2ISGW7vkcjRs3dyF8+XkjXJsnUiA4PdklS1Zh5I2COxufzRu1ExFZeTcfKucrpv1Rc7F0fnP5WXOyNyHHOC7pwh24ftq348G1HO/aox7uffpyjhXTFBd7ooYgYCI899OcYHuOFPgdHXvkqf5R3SIVDhUWO8DDzmpSBppWJiMhJz8yIjwrnko4ZnNsunRcmruaJL5cx4N+TePySdvzytHrBLlFE5KQS7vfx8IVtOP/pb3n4iyX844IT6LUjUlVVVq/DI3si+Xxe3yc5ZiHVXSr/4Mih/bleYy1/SGVfIiIiJyQmIoxb+2byt1+0AuCOd+fy/MSVfL+vjKu8iIhIuWhbrybXnd6It6auY+qq7cEuR0QktMKhwiJHtNsPU0dAastglyMiIlIlXdOtIZ/f2gOAf4xewql/H8sr364OclUiIieX2/s1IyMxmj9+OJ8DBSHQI0VEqrWQCoeKnSPtQOCH24xOwS1GRESkCjslPYEPbuzGwDa1AfjbJ4toePdnXP3yNO79eAEPjl5MfmFxkKsUEQldMRFh/OOCNqzatpenvloR7HJE5CQXUvOuHBBbkOvdaXNRUGsRERGp6jo0qEWHBh3Yub+AG/8zk+9WbmfishwmBvY/N3EVL1ydRb+WaUGtU0QkVPXITOGXp9VlxISVnNM2XQsFiEjQhNTIIRzEFQbCodjU4NYiIiJSTdSIDuet33Rh3l/P4r5BrRh/Zy9+f3ZzAH7z+gz+O3tDkCsUkerAzPqb2VIzW2Fmd5eyv4WZTTazPDO7syznmlmimY01s+WB61qV8Voq0z3ntKRmTAS3vD2bffmFwS5HRE5SIRUOFTtHQv4WMD/EpgS7HBERkWolISqcq7o2pGFyLDf3bsqcv/SjU6NE7vpgHu/PzOZAQZH6YohIqczMDzwNDABaApeb2ZFNQHOBW4BHj+Hcu4FxzrlMYFzgfkipFRvBvy49lRU5e/jLxwuDXY6InKRCKhxyQMq+lZDUFMIigl2OiIhItVYzJoIRv+pAk5Q47nxvLi3u+YLej45n884DHCgoYm+evuEWkR90AlY451Y55/KBkcCgkgc457Y656YDBcdw7iDgtcDt14DzK+oFBNPpmcn8tndT3p+Zzfszs4NdjoichEKr59DBkUN1GgS7FBERkZCQGBvBJ8O68+bUddw7aiGbdh6gy4PjAKifGEODpBg2fr+fV67tRP2kmCBXKyJBVBdYX+J+NtC5HM5Nc85tAnDObTKzkO0dcWvfZkxbk8s9/11A23o1aJYWH+ySROQkEnIjh6IKd0F0YrBLERERCRlhfh/XdGvI6gcH8t7QrjRKjgVgXe4+Ji3fxsqcvfR85GseHL04yJWKSBBZKdtcJZzrPYDZEDObYWYzcnJyjuXUKsPvM4Zf1p7YyDB+/ep0cnbnBbskETmJhNjIoYPhUMj1qRMREQk6M6Njw0S+vrMXADPX7mBvXiFJcRH88pnveG7iKt6fmU37+rUI9xt/PrcldWtGB7doEaks2UBGifv1gI3lcO4WM0sPjBpKB7aW9gDOueeB5wGysrKOKViqSlITonjpmiwue34K1702nZFDuhATEVK/solIFRUyI4ecc/gpIrJor8IhERGRStChQS16NkuhVZ0afHd3H9pl1GT3gUK+XLyFzxdspv+/JnL/p4uYvHK7+hOJhL7pQKaZNTKzCOAyYFQ5nDsKuCZw+xrg43KsuUpql1GT4Ze3Z8GGndzy9hwKi4qDXZKInARCJoZ2DmLZ792J1PxcERGRypQUF8n7Q7tSVOzYk1fI10u2cveH83nxm9W8+M1qwv1G/9bpDGhdmwGta2NW2iwSEamunHOFZjYMGAP4gZedcwvNbGhg/wgzqw3MABKAYjO7DWjpnNtV2rmBh34IeNfMrgPWARdX7isLjn4t0/jrL1rxl48Xctf783j04nb4fHrfFJGKU6ZwyMz6A//Ge7N+0Tn30BH7awD/AeoHHvNR59wrgX1rgN1AEVDonMsqt+pLcEAYgVTdH14RTyEiIiI/IdzvI9wPUeF+Ls7KoEdmCut37OOb5dtYu30vn87bxCdzN9K+fk3eH9oNv37REQkpzrnRwOgjto0ocXsz3pSxMp0b2L4dOLN8K60eru7akJ37Cnhs7DIiw/3844LWCtZFpML8bDhkZn7gaaAf3nzg6WY2yjm3qMRhNwOLnHPnmVkKsNTM3gwsRQnQ2zm3rbyLL+ngtDIAfCEzIEpERKTaql0jito1oujY0Fso4qEL23Lp81OYve57ejz8FQ9e2Jaemcn6ZUdE5CiG9WnK/oIinhm/ksgwH/ee11LvmSJSIcqSonQCVjjnVgGY2UhgEFAyHHJAvHnvVHFALlCpzQUcEK5wSEREpMqKCvfz8c3d+WBmNn/673yueXkaAGZw4xlN6NokiS6Nkwj3h0xLRBGRE2Jm/P7s5hwoKOblb1cTHeHnrrObKyASkXJXlhSlLrC+xP1soPMRxzyF1yxuIxAPXOqcO9g5zQH/MzMHPBdYSaDcOQd+UzgkIiJS1V3YoR5t69XgzanrePW7NTgHz4xfyTPjV5ISH0nO7jwGtqnNjWc0pU29GsEuV0QkqMyMe849hbzCIp4dv5LocD+3nJkZ7LJEJMSUJUUpLZY+cnnIs4E5QB+gCTDWzCY553YB3Z1zG80sNbB9iXNu4o+exGwIMASgfv36x/IaAgW5Qz2HFA6JiIhUaZlp8fz1F624tltD6tSM5vGxy5i1bgfTVucCMHr+ZiYt28bwy9vTu0VqkKsVEQkuM+O+Qa05UFDM42OXERXuY0jPJsEuS0RCSFlSlGwgo8T9engjhEoaDDzknHPACjNbDbQApjnnNgI457aa2Ud409R+FA4FRhQ9D5CjoHSIAAAgAElEQVSVlXVk+PSznONQzyG/wiEREZHqoGFyLAB3D2gBQH5hMV8u3kKYz7jpzVkMfnU6z155GgPapAezTBGRoPP5jH9e1Ja8wiL+MXoJgAIiESk3ZZnUPx3INLNGZhYBXIY3haykdQRWETCzNKA5sMrMYs0sPrA9FjgLWFBexZfknHoOiYiIVHcRYT4GtknnrFa1efLy9gDc+OYsrn55Gos37SK/sJj8wmK876NERE4ufp/xxKWnck7bdP4xegkPfb5E74ciUi5+NkVxzhWa2TBgDN5S9i875xaa2dDA/hHAfcCrZjYfbxraH5xz28ysMfBRoGFaGPCWc+6LinghDq1WJiIiEkoGtEnnf7f35O+fLGLW2h0M+PckwGtgfVr9Wgy/vD11a0YHuUoRkcoV7vcx/LL21IwOZ8SElezYm88DF7QmTM38ReQElClFcc6NBkYfsW1Eidsb8UYFHXneKqDdCdZYJs6hnkMiIiIhpllaPP+5vjM79ubz73HLeWPKWuKjwpi5dgf9Hp/Azb2b8v7MbK7sXJ/B3Rvh92kFHxEJfX6fcf/5rUmKi2T4uOV8vz+ff1/Wnqhwf7BLE5FqKmTiZQeEUejd8elNUUREJJTUio3gr79oxcp/DGTOX87iiUvbsS+/iEfGLGX1tr3c/9liLnt+Mos27iKvsCjY5YqIVDgz445+zbj3vJaMWbiFa1+Zxs79BcEuS0SqqZAZYuOcI8wOjhwKD24xIiIiUqEuaF+Pfi1rs2NvPl8u3sLfPlnE9DU7GDh8EvGRYQzp2ZjrejTCZ6Zv0kUkpA3u3ohaMRHc+d5cLnr2O16+tiMZiTHBLktEqpmQGTlUXHK1Mk0rExERCXlxkWFkJMYwuHsjVj84kGeuPI1LsupRKzaCx8Yuo+VfxnD6w1+zc5++SReR0HZ++7q8fl0ntu7O4/ynv2Xm2h3BLklEqpmQCYdQzyEREZGTlpkxsE06/7yoHSOHdOGX7esSHxnGtj15/OqlqRwo0FQzEQlt3Zok8+FN3YiLCuPyF6Ywau7GYJckItVIyIRDDqeeQyIiIkKdmtE8fumpzP/b2TxyUVvmb9jJwH9P4sNZ2RQUFQe7PBGRCtMkJY6PburOqfVqcsvbs3no8yUU6n1PRMogdMKhkiOH/Oo5JCIiInBxVgav/7oTe/MLuePdubT/+1iem7CSrbsPsHX3gWCXJyJS7hJjI3jj+k5c2bk+Iyas5OqXp7F9T16wyxKRKi50wiHUc0hERER+rGezFCbd1Yfb+zZjT14hD36+hE4PjKPTA+Po/eh4Xvl2Nc65YJcpIlJuIsP8PHBBG/55UVtmrN3BeU9+w9z13we7LBGpwkInHHKOMIVDIiIiUoqIMB+39s3kw5u6ceuZmbRMT6BHZjKrt+3lb58s4v7PFlNcrIBIRELLJVkZfHhjN8yMi0dM5u1p64JdkohUUSETDhU7qGl7vDsRscEtRkRERKqk0+rX4vZ+zRh9aw/euK4zo4Z1B+Clb1aT+efPmbY6l627D2gKhoiEjNZ1a/Dpb0+nc+NE/vjhfP7w/jw16ReRHwmZcMjhaGHryAuvAfHpwS5HREREqoG29WryyuCONEuLo6jYcclzk+n0wDg63P8lczQFQ0RCRK3YCF4d3IlhvZvyzoz1XPLcZLJ37At2WSJShYRMOISDmraX/ZHJYBbsakRERKSa6N08lTG39eTVwR355Wl1f9h+/WvTeXfGej6bt4m8Qn3LLiLVm99n3Hl2c164OovVOXs578lv+Hz+pmCXJSJVRMiEQw4Ip5Bin1YqExERkWNjZvRqnsrjl5zKmofOYfQtPagRHc5d78/j5rdm0fzPX7B5p1Y3E5Hqr1/LNEb99nTq1YrhxjdncevI2ezcVxDsskQkyEInHHIQThHFpnBIRERETkzLOgmMvrUHb13fmau6NADg2lemkV9YHOTKREROXKPkWD68qRu3923GZ/M2cda/JjBxWU6wyxKRIAqdcAhHOIU4rVQmIiIi5SAyzE+3psncd35rbjkzkyWbd9Psz5/z2P+WBrs0EZETFu73VnH8783dSYgK5+qXp3Hfp4vUrFrkJBUy4VCxgzAr0rQyERERKXe3nZnJ7X2bAfDkVyvIuv9Lhr01i5HT1rFjb36QqxMROX6t69bgk9+eztVdG/DSN6sZ+O9JzFybG+yyRKSShUw45Fxg5JBp5JCIiIiUL5/PuLVvJgv+djbN0+LZtiePT+dt4u4P59P+vrHc/NYsvlu5jXnZ31NU7IJdrojIMYkK9/P3Qa35z3WdySss5qIRk7n/00Xsz9coIpGTRcgkKV7PoUKKNa1MREREKkhcZBif39qD7/cXsGjjLt6evo552d/z2bxNfDbPW/WnV/MU/jTwFDLT4oNcrYjIsTk9M5kxt/fk4c+X8OI3qxm3ZCsPX9iWTo0Sg12aiFSwkBk5BBCGppWJiIhIxfL5jMTYCE7PTObpK05j0l19mPZ/ZzKsd1MAxi/Nod8TE3ntuzU4p1FEIlK9xEWGcd/5rXnrN50pLC7mkucm8/v35pKrKbQiIS1kwiHnIIJCnMIhERERqWSpCVHceXZzVj84kFcGdyQjMZp7Ry2k0R9Hc9ObM7nu1ens3K+lokWk+ujWJJkxt/Vk6BlN+Gj2Bvo8Np53pq+jWFNnRUJS6IRDOMIoUjgkIhIEZtbfzJaa2Qozu7uU/WZmwwP755nZaYHtGWb2tZktNrOFZnZr5VcvUn7MjN7NU/lgaDeapsYBMHr+ZsYt2cqgp75h1NyNQa5QpOKcwGdBczObU+Kyy8xuC+z7q5ltKLFvYGW/rpNZTEQYdw9owWe39CAzNY4/fDCfS56bzJLNu4JdmoiUs5Bp0FPsINIKKVDPIRGRSmVmfuBpoB+QDUw3s1HOuUUlDhsAZAYunYFnA9eFwO+cc7PMLB6YaWZjjzhXpNpJTYjiyzvOYMuuA9SKieCrJVu47Z053PL2bL5dvo2/nNeS2Ej9zCKh40Q+C5xzS4FTSzzOBuCjEuc94Zx7tOJfhRxN89rxvDOkK+/PzObBzxdzzvBv+HX3htzatxlxei8TCQmhM3LIOcIpolirlYmIVLZOwArn3CrnXD4wEhh0xDGDgNedZwpQ08zSnXObnHOzAJxzu4HFQN3KLF6kIqUlRBER5qN/63Sm/6kvGYnRvDNjPa3uHcNzE1YGuzyR8nTcnwVHHHMmsNI5t7biS5Zj4fMZl3TMYNzvenFxh3q8MGk1Zz42nk/nbVR/NZEQEDrhEGhamYhIcNQF1pe4n82PA56fPcbMGgLtgalHPoGZDTGzGWY2IycnpxxKFql88VHhTPx9b16+NguABz9fwh3vzCFnd16QKxMpF+XyWQBcBrx9xLZhgWloL5tZrfIoVo5fYmwED13Ylg9v6kZyXCTD3prN5S9MYfa6HcEuTUROQOiEQz8sZa9wSESkklkp2478CvEnjzGzOOAD4Dbn3I8aGTjnnnfOZTnnslJSUk6oWJFgMjP6tEjjyzt6AvDh7A10fOBLznvyG+au/z7I1YmckPL4LIgAfgG8V2L/s0ATvGlnm4DHSn1yfYlQ6U6rX4tRw07n74NasXzLHi545jtueGMGK7buDnZpInIcQiYcAoefYjB/sAsRETnZZAMZJe7XA47sunvUY8wsHC8YetM592EF1ilSZTRNjWf8nb144tJ2nNeuDvM37OT8Z77l2lemsXXXgWCXJ3I8TuizIGAAMMs5t+XgBufcFudckXOuGHgBb/raj+hLhODw+4yruzZkwl29ub1vM75dsZ2znpjIXe/PZeP3+4Ndnogcg5AJh5wDHw58pX0hISIiFWg6kGlmjQLf+l4GjDrimFHA1YGVaroAO51zm8zMgJeAxc65xyu3bJHgapgcywXt6/Hk5e35/NYeNE6OZfzSHPo9MZHH/7eUi0d8x5eLtvz8A4lUDcf9WVBi/+UcMaXsiJ5EFwALyr90OVFxkWHc2jeTCb/vxeDujfjv7I30enQ8D3y2iB1784NdnoiUQch0by524I1KDZm8S0SkWnDOFZrZMGAM4Adeds4tNLOhgf0jgNHAQGAFsA8YHDi9O3AVMN/M5gS2/Z9zbnRlvgaRYDslPYFxv+vFu9PXc9cH8xj+1QoApq+ZAcA957bkutMbBbNEkZ90gp8FmFkM3kpnNxzx0P80s1PxftBfU8p+qUKS4iK559yWDO7ekH99uZyXvlnNyGnrueGMxvz69EbERITMr58iIceqYmf5rKwsN2PGjGM6Z8nmXTR+tiHrW/yaJpdrpUsRCU1mNtM5lxXsOoLpeD4jRKqTlTl72J9fRL1a0VzxwlQWbTrUhistIZKHftmWJilxZCRG4w2+EzlEnxP6nKhKlm3ZzSNjljJ20RaS4yK59cymXNqxPhFh+kJfJFiO9jkRMtGtcwc73OmHJBEREam+mqTE/XB79K09yN2bz1lPTGTbnjy27Mpj8KvTAQjzGX1PSeOWMzNpWSchWOWKiBxVs7R4Xrg6i5lrd/DwF0u45+OFjJiwiht7NeHirHpEhqlfrEhVETKRrddzqBgsZF6SiIiICImxEUz5Yx8W/f1snry8PRmJ0dSpEYXfZ3yxcDMDh0/ioc+XMGNNLlVxRLiISIcGtXhnSBdeHdyR1IRI/vzfBZzxz/G8+u1qDhQUBbs8ESGURg7hMAj8V0RERCR0hPl9hPl9nNeuDue1q/PD9hlrcrloxGRGTFjJiAkrqRUTjplxe99MruraMHgFi4gcwczo1TyVM5ql8O2K7Qz/ajl//WQRT329kpt6NeHKLvU1kkgkiEInHHLgM6e59yIiInLSyGqYyOoHBzJp+TbGL81h+dbdrMvdxz0fL+SB0Ys5UFDM1V0b8PuzmxMfFR7sckVEMDNOz0zm9Mxkpq7azr++XM7fP13E8xNX8Zuejbm8U4YaV4sEQcj8q/thFLXCIRERETmJmBk9m6XQs1kKAKu37eWZr1eQV1jMqLkbeX3yWiYsy+GzW3oQFxkyP/qJSAjo3DiJt4ck8e2KbTz51XLu+3QRT321nF93b8TVXRtSI0ahtkhlCZkGPc4VezfUc0hEREROYo2SY3nk4nYMv7w9k+7qzeWd6rN2+z76PDqeMQs3B7s8EZEf6d40mZFDuvLBjV05rX4tHhu7jO4Pf8WDny9my64DwS5P5KQQMkmKK/bCIU0rExEREfFkJMbw4C/b8OLVWSREh3PL27OZtW5HsMsSESlVhwaJvHRtR0bf0oPeLVJ5YeIqTn/4K+58by5LN+8OdnkiIS10wiF+mFcW1DpEREREqpq+LdN4/dedSIyN4JfPfMfHczYEuyQRkaNqWSeBJy9vz9d39uKKTvX5bN4mzv7XRH714lS+XLSFomKtzChS3kJm4vmhaWUKh0RERESOVKdmNNed3oj7P1vMrSPnsD+/iD15haTXiOactunBLk9E5EcaJMXyt0Gtua1vM96ato43Jq/l+tdnUD8xhqu7NuDirAxqRKsvkUh5CJlw6IeO1AqHREREREp1fY/GnNWyNle/PJW7P5z/w/a0hK50aFCLnN15pCZEBbFCEZEfqxUbwc29mzKkZ2PGLNzMq9+u4f7PFvP42GVceFo9runWkKapccEuU6RaC5lwSA2pRURERH5e/aQYRv32dF75Zg3Na8fzu3fncNGIyT/sb5meQN+WadzeN1O9HEWkSgn3+zi3bR3ObVuH+dk7efW7NbwzfT1vTFlLj8xkBndvSK9mqfh8eu8SOVahEw4F5p2aeg6JiIiI/KSEqHBu7ZsJQOOU7nw6dyMbdx5gyeZdLN60m0WbdtEyPZ7+rTXdTESqpjb1avDYJe3448AWjJy2jjemrOXXr86gQVIMV3dtyIWn1aVmTESwyxSpNkImHAJNKxMRERE5Vs3S4rnjrOY/3C8qdpz9r4kM/c8s+rVMo3ZCFNd2b0iTFE3ZEJGqJzkukmF9MrnhjCZ8sWAzr363hvs+XcTDXyxhQOvaXNoxgy6NkjSaSORnhEw45Iq1WpmIiIjIifL7jKevOI0b3pjBhGU55BcW88aUtdSpEUVWw0Tuv6A1CVFqACsiVUu438d57epwXrs6LNq4i3emr+Oj2Rv4eM5GGiTFcElWBhd3qKe+aiJHETrh0MGeQz71HBIRERE5Ec1rxzP+971xzjF1dS73fbqIhRt3MWruRqau3s7jl5xK96bJwS5TRKRULesk8LdBrfnjwFP4YsFmRk5fxyNjlvL42GX0bp7KZR0z6NU8hTC/fncUOahM/xrMrL+ZLTWzFWZ2dyn7a5jZJ2Y218wWmtngsp5bbgLhkHoOiYiIiJQPM6NL4yQ+u6UHK/8xkGevPI2iYseVL07l/ZnZwS5PROQnRYX7Ob99XUYO6crXd/ZiSM/GzFn/Pde/PoPuD3/Fo2OWsm77vmCXKVIl/Gw4ZGZ+4GlgANASuNzMWh5x2M3AIudcO6AX8JiZRZTx3HJRrKXsRURERCqM32cMaJPO/24/g04NE7nzvblk3f8ld38wj5lrc4NdnojIT2qUHMsf+rdg8h/78NxVHWhVpwbPjF9Bz0e+5ooXpvD+zGz25BUGu0yRoCnLyKFOwArn3CrnXD4wEhh0xDEOiDdvvdM4IBcoLOO55UPhkIiIiEiFS4yN4JXBHbm4Qz2iI3y8O2M9Fz47me4PfcVHsw+NJir+oR+kiEjVEe73cXar2rx8bUe+vbsPv+vXjOwd+7nzvbl0vP9Lbhs5mwnLcigsKg52qSKVqiw9h+oC60vczwY6H3HMU8AoYCMQD1zqnCs2s7KcC4CZDQGGANSvX79MxZeUFu8tU5gQpeUKRURERCpSbGQYj1zcDoDv9+Xz8jerGf7VCm5/Zy5vTV3Hiq172JtfxLlt0nnk4nb4tUqQiFRB6TWi+e2ZmQzr05SZa3fw4ewNfDp3I/+ds5GU+EgGtavD+e3r0qpOAqZBCBLiyhIOlfav4Mivgs4G5gB9gCbAWDObVMZzvY3OPQ88D5CVlXXMXzU1TIoBIDFO3edFREREKkvNmAjuOKs5N/dpyu/fm8eouRsBqBUTzoezNzBuyVb6tEjlpl5NyEyLD3K1IiI/ZmZkNUwkq2Ei957Xkq8Wb+XD2Rt4bfIaXvxmNY1TYhnUri6/OLUOjZJjg12uSIUoSziUDWSUuF8Pb4RQSYOBh5xzDlhhZquBFmU8t3xoWpmIiIhI0ESG+Rl+eXsevbgdW3YdoF6taO77dDGfzNvIR7M38MWCzTxx6an0b1072KWKiBxVZJifAW3SGdAmnR178/l8wWY+nrOBf41bxhNfLqNlegLntE1nQOvaNE6JC3a5IuWmLD2HpgOZZtbIzCKAy/CmkJW0DjgTwMzSgObAqjKeW04ODjZSOCQiIiISLBFhPjISYzAz/nJeS6b/qS8f3NiNBkkxDP3PTL5dsU29PESkWqgVG8EVnevzzg1d+e7uPvz5nFOICvfxyJil9HlsAv3/NZHh45azYuvuYJcqcsJ+duSQc67QzIYBYwA/8LJzbqGZDQ3sHwHcB7xqZvPx0pk/OOe2AZR2boW8Eo0cEhEREamSOjSoxd0DWnDtK9O58sWp1IgO57a+mfyiXR2S4iKDXZ6IyM9KrxHN9T0ac32PxmzauZ8vFmxm9PxNPPHlMh4fu4zM1DgGtklnYJt0mqXFqUeRVDvmXNVbSSIrK8vNmDHj2E7asxUezYRzHoOO11dMYSIiQWZmM51zWcGuI5iO6zNCRKqEz+Zt4ua3ZmF26Hu9d4Z0oXPjpOAWFkL0OaHPCalcW3Yd+CEomrYmF+egSUrsD0FRi9rxCoqkSjna50RZeg5VD+7g8GT9wxMRERGpis5pm845bc/BOcdb09bxp48WcOnzU0ivEcXIIV1okKRGryJSvaQlRHFNt4Zc060hW3cfYMzCLXw+fxNPf72CJ79aQaPkWAa0rs3ANula9UyqtBAKhzStTERERKQ6MDOu7NyATg0TeWD0YsYvzeHql6fx8IVt6aJRRCJSTaXGR3FVlwZc1aUB2/bk8b+FW/h8wSaem7iKZ8avpH5iDANa16ZfyzTa16+F36ffXaXqCJ1wSA2pRURERKqVzLR4Xh3cid+/N5f3ZmZz2fNTuPe8lgzu3ijYpYmInJDkuEiu6FyfKzrXJ3dvPmMXbeaz+Zt56ZvVPDdxFYmxEfRunkrfU1Lp0SyFuMgQ+tVcqqXQ+RuokUMiIiIi1dIjF7fjko4ZXDxiMn/7ZBH/+nI557ZN5/7zW2sKhohUe4mxEVzasT6XdqzPrgMFTFyWw7jFW/ly8RY+mJVNhN9H58aJ9D0ljT4tUslIjAl2yXISCp1w6ODIIfMFtwwREREROWYdGyYy+55+vD8zm6e+XsGbU9fx5tR1TP9TX1LitaJZdWBm/YF/461S/KJz7qEj9ltg/0BgH3Ctc25WYN8aYDdQBBQebJZqZonAO0BDYA1wiXNuRyW8HJEKkRAVzrlt63Bu2zoUFhUzc+0Oxi3ZypeLtnDvqIXcO2ohmalx9DkllT7NU+nQoBZhfv2OKxUvdMIhNaQWERERqdZqxUbwm56N6XNKKmc+NgGAB0cv5vFLTw1yZfJzzMwPPA30A7KB6WY2yjm3qMRhA4DMwKUz8Gzg+qDezrltRzz03cA459xDZnZ34P4fKuhliFSqML+Pzo2T6Nw4if8beAqrcvbw1ZKtfL10Ky9NWs1zE1ZRIzqcns1SOLNFKmc0S6FWbESwy5YQFULhkKaViYiIiISCJilxvDK4Ix/N2sCHszcQE+nn/vPbBLss+WmdgBXOuVUAZjYSGASUDIcGAa875xwwxcxqmlm6c27TTzzuIKBX4PZrwHgUDkmIapwSR+OUOK7v0ZjdBwr4Zvk2xi3ZyvilW/lk7kZ8BqfVr0XvFqn0aZFKi9rxmnor5SZ0wiE1pBYREREJGb2bp9K9STJrc/fxnynr+M+UdbStV4P/XN+ZhKjwYJcnP1YXWF/ifjaHjwo62jF1gU14P8z/z8wc8Jxz7vnAMWkHwyPn3CYzS62I4kWqmviocAa0SWdAm3SKix3zN+xk3JKtfL1kK4+MWcojY5aSGh/J6ZnJ9MxMoXvTZE3BlRMSOuGQU88hERERkVASEeZj5G+6MOSNGUxavo152Tv538ItXNShXrBLkx8r7RtadwzHdHfObQyEP2PNbIlzbmKZn9xsCDAEoH79+mU9TaRa8PmMdhk1aZdRkzv6NWPrrgOMX5rDpBXb+HrJVj6ctQGAlukJ9GjmhUUdGtQiKtwf5MqlOgmhcCjQc0jD6kRERERCRnSEnxeuzmJ97j6ue20Gd70/l7emrqV/69oYxjXdGhIRpi8Hq4BsIKPE/XrAxrIe45w7eL3VzD7Cm6Y2EdhycOqZmaUDW0t78sBIo+cBsrKyjgylREJKakIUl3TM4JKOGRQXOxZu3MXE5TlMWp7Dy994vYqiwn10bpREj8xkejZLITM1TlPQ5CeFTjj0A/2FFxEREQklUeF+MtPief/GrvzmtRnMWvc9s9Z9D8Dizbu49cxMGiTFBrnKk950INPMGgEbgMuAK444ZhQwLNCPqDOwMxD6xAI+59zuwO2zgL+XOOca4KHA9ccV/1JEqg+fz2hTrwZt6tXg5t5N2ZtXyNTV25m4bBuTludw/2eL4bPFpCVE0q1JMl2bJNGtSRL1asUEu3SpYkInHFJDahEREZGQlhofxfs3dmPt9n0s3rSLuz+Yx4ezNvDhrA3c2KsJf+jfItglnrScc4VmNgwYg7eU/cvOuYVmNjSwfwQwGm8Z+xV4S9kPDpyeBnwUGNUQBrzlnPsisO8h4F0zuw5YB1xcSS9JpFqKjQyjT4s0+rRIA2DD9/v5ZnkOk5Z7YdFHs70paBmJ0XRrnEy3pkl0bZxEakJUMMuWKiB0wiE1pBYREREJeeF+H01T42iaGsc5bdL5YFY2z05YybPjV7Iudx8+M/q3qs3ANrU1haKSOedG4wVAJbeNKHHbATeXct4qoN1RHnM7cGb5Vipy8qhbM5pLO9bn0o71cc6xfOsevluxjcmrtvP5gk28M8PrEd80NY6ujb1RRV0aJ1ErNiLIlUtlC51wSCOHRERERE4qPp9xcVYGZ56Sxg1vzGDswi3kFxXzydyN9GmRygtXZ+H36WdDEREAM6NZWjzN0uK5tnsjioodizft4ruV2/hu5XY+nJXNG1PWYgan1E6gW5MkujZJolOjROK1SmTIC6FwSA2pRURERE5GibERvDe0G845ducV8uiYpbw+eS29Hv2aYb2bMqBNOgn6xUZE5DB+n9G6bg1a163BkJ5NKCgq5v/bu/foqsozj+PfJyEkkJxcCARCAgSSICAgCAoIVVtbBx0t9jKztKXXaamdMrXjrLZ25g87rlmz2o610y5bHSpKbRXqUtvSVi221lpFuQkCcg23EEi4E0Is92f+2JtwiAFyMydnn99nrbPOPu/ZO3mfk3POs/Ls93336pp6Xg+LRT9/YwePvLqN9DRjTEkek4b2YWJZHyYOKdDIogiKTnFI08pEREREUpqZkZuVwb23Xs7rWw6wee9RvvnMGh756zZ++umJnDx9hsr+sUR3U0SkW8pIT2PCkAImDClg9gcqOXbyNCurD/P6lmAa2mOvbef/XtkKBNPQriorYMKQPlxVVsDgPr01lTfJRac4pGllIiIiIkJwNvzFu6+j/m8n+dP6Pdz91Ftcf//LAFw7vB/3ffhyyvrq6mYiIheTlZHOlHBqGcCxk6dZs6ueZdsPsnz7IX6/upb5S4M1i/rFMpk4pICJZUGxaGRxLhnpaYnsvrRRdIpDZ0cOmd6AIiIiIgJ5vTL46JWlDO8f48ml1Wysa+CNrQe4/v6X+efry5n9gQqOnzyj6REiIq2QlZHOVWV9uKqsDwBnzgQLXC/fERSLlm47yPNr6wDolZHO+EeQjxIAABFtSURBVMH5TQWj8YPztW5RNxed4tDZNYc0rUxERERE4owuyeO/PzIGgBfW1nLnL97kJy9v4Scvb2naZ8GsyUweVpioLoqIJJ20NOOyATEuGxDjk5OGAFBXf6ypWLR8x0Ee/HMVZxzSDC4bkMv4wfmMH5TP+MEFDOubTZouGtBtRKg4pGllIiIiInJx00cX89a9N7JgaTVzXtnKgcYTAMx8ZAk3jCzi3lsvZ2B+rwT3UkQkOQ3Iy+KWsQO5ZexAAI4eP8Wq6sMs33GQFTsO8du3dvPkkmoAYlk9GBcWisYPymfcoHyN5Eyg6BSHtCC1iIiIiLRCXq8MvnRdOV+6rhyAzXsaeOgvW3hhbR3XfOclCrN7csPIIj4+YRAlBb0oUbFIRKRdcjJ7MK2yL9Mq+wLBVLSt+xtZWX2IlTsPs7L6MA++tJkz4b/zQ/tmhyOL8hk3qIARxTGtXdRFolMc0sghEREREWmHyv4xHvjHcXzxfUf45bKd7G04xlPLa3hqeQ0Ad39oOF95fwXpmv4gItIhaWlGRVEOFUU5/MPEQQA0Hj/Fml31rKw+zMrqQ/y1aj/PrtwFQGaPNMaW5p0bYTQ4n+I8FezfC9EpDmlBahERERHpgJHFuXz7w5cDsGXfUf64bg/LdxzigRc38fzaOj4/tYyPjC+hh85ii4h0muzMHkweVti07pu7s7v+WDC6qPowq3Ye5mev7+Cnf90GQFEskzEleYwpzWNsaR6jS/IoimUlMoRIiE5xSAtSi4gkjJlNB34IpAOPuPt3mj1v4fM3A+8An3X3N8PnHgVuAfa6++gu7biIyAWU98uh/LocZrnzm1W7uX/RRr7+9Gq+v2gT7x/Rjy+8bxjvHD/NmNK8RHdVRCRSzIyS/GBK79m1i06cOsP62iOsrD7E6l31rKmp56WNe5smEA3IzWJMaV5T0WhMSR59czITGEXyiVBxKLzXtDIRkS5lZunAj4EPATXAMjNb6O7r4na7CagMb5OAh8J7gHnAg8DjXdVnEZHWMjNuG1/CrVcMZO6rW3n01e3MX7qT+Ut3AjB+cD5fvq6ca4f3IysjPcG9FRGJpp490rhiUD5XDMpvams8fop1tUdYXVPPmprDrNlVzx/X72kqGJXk92JsaR5XDMpnTEkeIwbEKFTB6IKiUxzSgtQiIolyNVDl7lsBzGwBMAOILw7NAB53dwfeMLN8Myt291p3f8XMyrq60yIibZGeZsy6tpxZ15Yzf2k1b+08zCub9rGy+jCzfr6CkvxeTK0o5OqhhXx8QmmiuysiEnnZmT24qqwPV5X1aWprOHaSt3cfYU1NPW/VHGZ1TT3Pr61rer4olsmI4lxGFscYOSCXkcW5DOuXrUWviVJxSAtSi4gkSgmwM+5xDedGBV1snxKgtjW/wMxmAbMABg8e3O6Oioh0hjuuHswdVwffRX87cZonluzg+4s28dTyGp5eUcOKHQeZPKyQW8cOJE2LWIuIdJlYVsZ56xcBHGw8wbrdR9hQd4R1tUfYUNvAY1sOcOJ0sDRNz/Q0KopyGFEcY1RxLiMGBMWjVBtlFKHiULjmkIpDIiJdraUvXm/HPhfk7nOAOQATJ05s9XEiIu+1Xj3T+cL7hjFz8hCOHj/FR3+yuGna2V0LVvG9j41l+pgB5GZlJLqrIiIpqU92T6ZV9mVaZd+mtpOnz7B1XyPra4+wvu4I62sbeHXzfp59c1fTPv1imYxMoVFG0SkOaVqZiEii1ACD4h6XArvbsY+ISNLKykgnKyOdhbOnsrfhOIur9vPt367jG8+s5hvPrGb+Fyczpbzw0j9IRETecxnpaVw2IMZlA2LcRklT+4Gjx9lQ1xAUjWqD+1QZZRSd4pCmlYmIJMoyoNLMhgK7gNuBTzTbZyEwO1yPaBJQ7+6tmlImIpJM8nv3JL93T4b3j3HTmGJ+8OImFizbycy5S/jMlDK+ekMFBxpPMLQwW1PORES6mcKcTKZWZDK14t2jjOKnpV1wlNGAGCOLcxlRHKO8X05SjTKKTnFII4dERBLC3U+Z2WzgDwSXsn/U3d82szvD5x8GniO4jH0VwaXsP3f2eDObD1wP9DWzGuBed5/btVGIiHS+/rlZfOdjY7nrg5XcNX8V8xZvY97ibZxx+PL15Xxz+ggajp0kpilnIiLdVvwooxnjLjzKaEPdER577dwoo4x0o6Iodt60tBHFMfp201FG0SkONY0cSp7KnIhIVLj7cwQFoPi2h+O2HfjKBY69473tnYhIYhXn9eKpO6ewbvcRFiyr5vHXd/DQy1v4w9o6tu5v5JOTBvOpKUMYMSA30V0VEZFWutAoo237G8+blvZa1fmjjAp6Z1BRlENFUQ7l/XKatgfm9UroiNIIFYe0ILWIiIiIdF+jBuZy34zR3DhqADPnLmHr/kYAnlhSzRNLqvnsNWXcfeNwLV4tIpKkMtLTGN4/xvD+MWaMO9d+sPEEG2qPsL6ugaq9DVTtPcoLa+s49M7Jpn16ZaRTXpRNRVzBqKIohyGFXbMIdnSKQ5pWJiIiIiJJYFplX373L9PIzuzBGXd+9KfN/GbVbuYt3s68xdv5/VencfnAvER3U0REOkmf7J5cU9GXa+JGGUEwNa1q71Gq9h0N7vceZem2g/x61bnrtvRIM4YU9qayKMbw/jlU9I8xcUgBA/N7dWofo1Mc0oLUIiIiIpIkRpecK/788PbxfPdjY/n8vGUs3nKAXy7byX0zVBwSEYm6wpxMCnMymTTs/KtZNh4/xdZ9jVTtC0YZbd5zlE17Gli0ro4zDv9122hmTh7SqX2JTnGo5EqY9RcoLE90T0RERERE2iQrI50nvziZFTsOcflArT0kIpLKsjN7MKY0jzGl558oOHbyNNv2N9Iv1vmLWkenOJQZg4HjLr2fiIiIiEg3NWFIQaK7ICIi3VRWRjoji9+bEwi6tJeIiIiIiIiISApTcUhEREREREREJIWpOCQiIiIiIiIiksJUHBIRERERERERSWEqDomIiIiISIeZ2XQz22hmVWZ2TwvPm5n9KHx+tZldGbYPMrM/m9l6M3vbzO6KO+bbZrbLzFaFt5u7MiYRkVQRnauViYiIiIhIQphZOvBj4ENADbDMzBa6+7q43W4CKsPbJOCh8P4U8G/u/qaZxYAVZvZi3LE/cPf7uyoWEZFU1KqRQ604C/D1uGr+WjM7bWZ9wue2m9ma8LnlnR2AiIiIiIgk3NVAlbtvdfcTwAJgRrN9ZgCPe+ANIN/Mit291t3fBHD3BmA9UNKVnRcRSXWXLA7FnQW4CRgF3GFmo+L3cff/cfdx7j4O+BbwF3c/GLfL+8PnJ3Zi30VEREREpHsoAXbGPa7h3QWeS+5jZmXAeGBJXPPscBrao2ZW0NIvN7NZZrbczJbv27evfRGIiKSw1owcas1ZgHh3APM7o3MiIiIiIpIUrIU2b8s+ZpYDPAN8zd2PhM0PAeXAOKAW+H5Lv9zd57j7RHef2K9fv7b2XUQk5bWmONSaswAAmFlvYDrBl/pZDiwysxVmNutCv0TVfhERERGRpFUDDIp7XArsbu0+ZpZB8D/EE+7+7Nkd3H2Pu5929zPATwlOXIuISCdrzYLUrTkLcNatwGvNppRNdffdZlYEvGhmG9z9lXf9QPc5wBwAM9tnZjta0bfm+gL723FcslK80ZZK8aZSrNCxeId0ZkeS0YoVK/a3M0eA3mtRp3ijK5ViheTME8uASjMbCuwCbgc+0WyfhQRTxBYQLERd7+61ZmbAXGC9uz8Qf8DZNYnChx8B1l6qI8oTbaJ4oyuVYgXF2xYt5onWFIdacxbgrNtpNqXM3XeH93vN7FcE1f53FYeaHdOusaBmtjyV1jVSvNGWSvGmUqyQevF2tvbmCEi9117xRlsqxZtKsUJyxuvup8xsNvAHIB141N3fNrM7w+cfBp4DbgaqgHeAz4WHTwU+Bawxs1Vh27+7+3PA98xsHMHJ6e3Al1rRF+WJVlK80ZVKsYLi7QytKQ615iwAZpYHXAfMjGvLBtLcvSHcvhG4rzM6LiIiIiIi3UdYzHmuWdvDcdsOfKWF416l5dkKuPunOrmbIiLSgksWh1p5FgCCYZ6L3L0x7vD+wK+CkaL0AJ509xc6MwAREREREREREWm/1owcuuRZgPDxPGBes7atwBUd6mHbzOnC39UdKN5oS6V4UylWSL14u5NUe+0Vb7SlUrypFCukXrzdSaq99oo3ulIpVlC8HWbB6E4REREREREREUlFrbmUvYiIiIiIiIiIRFRkikNmNt3MNppZlZndk+j+dJSZDTKzP5vZejN728zuCtv7mNmLZrY5vC+IO+ZbYfwbzezvEtf79jOzdDNbaWa/Cx9HNl4zyzezp81sQ/h3nhLxeP81fC+vNbP5ZpYVpXjN7FEz22tma+Pa2hyfmU0wszXhcz8KL+8rnUB5Ivk+Vy1RnohmvMoRyhHdgfJE8n22WqI8Ec14lSe6IE+4e9LfCBbK3gIMA3oCbwGjEt2vDsZUDFwZbseATcAo4HvAPWH7PcB3w+1RYdyZwNDw9UhPdBztiPtu4Engd+HjyMYL/Az4QrjdE8iParxACbAN6BU+fgr4bJTiBa4FrgTWxrW1OT5gKTCF4KotzwM3JTq2KNyUJ5Lzc3WBuJUnIhavcoRyRHe4KU8k52frAnErT0QsXuWJrskTURk5dDVQ5e5b3f0EsACYkeA+dYi717r7m+F2A7Ce4EMxg+BLgPD+tnB7BrDA3Y+7+zagiuB1SRpmVgr8PfBIXHMk4zWzXIIvgLkA7n7C3Q8T0XhDPYBeZtYD6A3sJkLxuvsrwMFmzW2Kz8yKgVx3f92Db/fH446RjlGeSMLPVXPKE5HOE8oRyhGJpjyRhJ+t5pQnlCfC7aSLtTvkiagUh0qAnXGPa8K2SDCzMmA8sATo7+61EHzhA0XhblF4Df4X+AZwJq4tqvEOA/YBj4XDXh8xs2wiGq+77wLuB6qBWqDe3RcR0XjjtDW+knC7ebt0XFTeUy1SnohkvCmTJ5QjlCO6iai8r1qkPBHJeJUnIhhrM12aJ6JSHGppHl0kLsNmZjnAM8DX3P3IxXZtoS1pXgMzuwXY6+4rWntIC21JEy9B5ftK4CF3Hw80EgwVvJCkjjecHzuDYNjjQCDbzGZe7JAW2pIm3la4UHxRjzuRIvvaKk9c+JAW2pImXlIoTyhHvItyRGJE9vVVnrjwIS20JU28KE8oT5yvw3kiKsWhGmBQ3ONSgmFmSc3MMgi+yJ9w92fD5j3hcDHC+71he7K/BlOBD5vZdoJhvB8ws18Q3XhrgBp3XxI+fprgyz2q8X4Q2Obu+9z9JPAscA3RjfestsZXE243b5eOi8p76jzKE8oTRCNe5QjliO4gKu+r8yhPKE8QjXiVJ7ogT0SlOLQMqDSzoWbWE7gdWJjgPnVIuKr4XGC9uz8Q99RC4DPh9meA38S1325mmWY2FKgkWIwqKbj7t9y91N3LCP5+L7n7TKIbbx2w08wuC5tuANYR0XgJhoBONrPe4Xv7BoJ571GN96w2xRcOF20ws8nh6/TpuGOkY5QnkvxzpTwR6TyhHKEc0R0oTyT5Z0t5QnmCaMQar2vzhHeDlbk74wbcTLAC/xbgPxLdn06IZxrBELDVwKrwdjNQCPwJ2Bze94k75j/C+DeSxFevAK7n3NUFIhsvMA5YHv6Nfw0URDze/wQ2AGuBnxOsrh+ZeIH5BHOgTxJU7f+pPfEBE8PXaAvwIGCJji0qN+WJ5PtcXSR25YmIxascoRzRHW7KE8n32bpI7MoTEYtXeeK9zxMW/gAREREREREREUlBUZlWJiIiIiIiIiIi7aDikIiIiIiIiIhIClNxSEREREREREQkhak4JCIiIiIiIiKSwlQcEhERERERERFJYSoOiYiIiIiIiIikMBWHRERERERERERSmIpDIiIiIiIiIiIp7P8BeyVX/XDRdmQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1440x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training until validation scores don't improve for 800 rounds\n",
      "[100]\ttrain's auc: 0.954118\ttrain's binary_logloss: 0.105884\ttrain's binary_error: 0.0343117\tvalid's auc: 0.9348\tvalid's binary_logloss: 0.114442\tvalid's binary_error: 0.0364372\n",
      "[200]\ttrain's auc: 0.974628\ttrain's binary_logloss: 0.0856923\ttrain's binary_error: 0.030803\tvalid's auc: 0.939921\tvalid's binary_logloss: 0.104145\tvalid's binary_error: 0.0333333\n",
      "[300]\ttrain's auc: 0.984844\ttrain's binary_logloss: 0.073215\ttrain's binary_error: 0.0275304\tvalid's auc: 0.940249\tvalid's binary_logloss: 0.100823\tvalid's binary_error: 0.0321188\n",
      "[400]\ttrain's auc: 0.990735\ttrain's binary_logloss: 0.0641416\ttrain's binary_error: 0.0254049\tvalid's auc: 0.940135\tvalid's binary_logloss: 0.0995269\tvalid's binary_error: 0.0329285\n",
      "[500]\ttrain's auc: 0.994205\ttrain's binary_logloss: 0.0567986\ttrain's binary_error: 0.022807\tvalid's auc: 0.940481\tvalid's binary_logloss: 0.0986873\tvalid's binary_error: 0.0319838\n",
      "[600]\ttrain's auc: 0.996426\ttrain's binary_logloss: 0.0507833\ttrain's binary_error: 0.0201754\tvalid's auc: 0.940842\tvalid's binary_logloss: 0.0980761\tvalid's binary_error: 0.0315789\n",
      "[700]\ttrain's auc: 0.997774\ttrain's binary_logloss: 0.0455732\ttrain's binary_error: 0.0167679\tvalid's auc: 0.942037\tvalid's binary_logloss: 0.0975837\tvalid's binary_error: 0.0319838\n",
      "[800]\ttrain's auc: 0.99861\ttrain's binary_logloss: 0.0410919\ttrain's binary_error: 0.0141026\tvalid's auc: 0.94235\tvalid's binary_logloss: 0.0974101\tvalid's binary_error: 0.0318489\n",
      "[900]\ttrain's auc: 0.999176\ttrain's binary_logloss: 0.037125\ttrain's binary_error: 0.0117409\tvalid's auc: 0.942435\tvalid's binary_logloss: 0.0974435\tvalid's binary_error: 0.0317139\n",
      "[1000]\ttrain's auc: 0.999542\ttrain's binary_logloss: 0.0336287\ttrain's binary_error: 0.00890688\tvalid's auc: 0.942582\tvalid's binary_logloss: 0.0975332\tvalid's binary_error: 0.0311741\n",
      "Did not meet early stopping. Best iteration is:\n",
      "[1000]\ttrain's auc: 0.999542\ttrain's binary_logloss: 0.0336287\ttrain's binary_error: 0.00890688\tvalid's auc: 0.942582\tvalid's binary_logloss: 0.0975332\tvalid's binary_error: 0.0311741\n",
      "dict_keys(['train', 'valid'])\n",
      "3\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training until validation scores don't improve for 800 rounds\n",
      "[100]\ttrain's auc: 0.956369\ttrain's binary_logloss: 0.104895\ttrain's binary_error: 0.034278\tvalid's auc: 0.925613\tvalid's binary_logloss: 0.117524\tvalid's binary_error: 0.0348178\n",
      "[200]\ttrain's auc: 0.975074\ttrain's binary_logloss: 0.0848149\ttrain's binary_error: 0.0305668\tvalid's auc: 0.93442\tvalid's binary_logloss: 0.10708\tvalid's binary_error: 0.0331984\n",
      "[300]\ttrain's auc: 0.98499\ttrain's binary_logloss: 0.0724646\ttrain's binary_error: 0.0273617\tvalid's auc: 0.938606\tvalid's binary_logloss: 0.10322\tvalid's binary_error: 0.0323887\n",
      "[400]\ttrain's auc: 0.990847\ttrain's binary_logloss: 0.0633598\ttrain's binary_error: 0.0247976\tvalid's auc: 0.938662\tvalid's binary_logloss: 0.102138\tvalid's binary_error: 0.0321188\n",
      "[500]\ttrain's auc: 0.994319\ttrain's binary_logloss: 0.0559313\ttrain's binary_error: 0.0222672\tvalid's auc: 0.940287\tvalid's binary_logloss: 0.100876\tvalid's binary_error: 0.0321188\n",
      "[600]\ttrain's auc: 0.996561\ttrain's binary_logloss: 0.0499008\ttrain's binary_error: 0.0195682\tvalid's auc: 0.941059\tvalid's binary_logloss: 0.100412\tvalid's binary_error: 0.0319838\n",
      "[700]\ttrain's auc: 0.997833\ttrain's binary_logloss: 0.0447351\ttrain's binary_error: 0.017004\tvalid's auc: 0.941636\tvalid's binary_logloss: 0.100069\tvalid's binary_error: 0.0321188\n",
      "[800]\ttrain's auc: 0.998635\ttrain's binary_logloss: 0.0403392\ttrain's binary_error: 0.0143387\tvalid's auc: 0.94186\tvalid's binary_logloss: 0.0999854\tvalid's binary_error: 0.0325236\n",
      "[900]\ttrain's auc: 0.9992\ttrain's binary_logloss: 0.0364252\ttrain's binary_error: 0.0111336\tvalid's auc: 0.942957\tvalid's binary_logloss: 0.0993615\tvalid's binary_error: 0.0326586\n",
      "[1000]\ttrain's auc: 0.999548\ttrain's binary_logloss: 0.0330247\ttrain's binary_error: 0.00853576\tvalid's auc: 0.943205\tvalid's binary_logloss: 0.0996565\tvalid's binary_error: 0.0326586\n",
      "Did not meet early stopping. Best iteration is:\n",
      "[1000]\ttrain's auc: 0.999548\ttrain's binary_logloss: 0.0330247\ttrain's binary_error: 0.00853576\tvalid's auc: 0.943205\tvalid's binary_logloss: 0.0996565\tvalid's binary_error: 0.0326586\n",
      "dict_keys(['train', 'valid'])\n",
      "3\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training until validation scores don't improve for 800 rounds\n",
      "[100]\ttrain's auc: 0.953951\ttrain's binary_logloss: 0.104722\ttrain's binary_error: 0.0343117\tvalid's auc: 0.926692\tvalid's binary_logloss: 0.117565\tvalid's binary_error: 0.0384615\n",
      "[200]\ttrain's auc: 0.974864\ttrain's binary_logloss: 0.0850675\ttrain's binary_error: 0.029892\tvalid's auc: 0.937633\tvalid's binary_logloss: 0.106955\tvalid's binary_error: 0.034413\n",
      "[300]\ttrain's auc: 0.984809\ttrain's binary_logloss: 0.0729059\ttrain's binary_error: 0.0274966\tvalid's auc: 0.941394\tvalid's binary_logloss: 0.103016\tvalid's binary_error: 0.0337382\n",
      "[400]\ttrain's auc: 0.990699\ttrain's binary_logloss: 0.0638017\ttrain's binary_error: 0.0248988\tvalid's auc: 0.943981\tvalid's binary_logloss: 0.101106\tvalid's binary_error: 0.0340081\n",
      "[500]\ttrain's auc: 0.994341\ttrain's binary_logloss: 0.0565415\ttrain's binary_error: 0.0221323\tvalid's auc: 0.94535\tvalid's binary_logloss: 0.0998857\tvalid's binary_error: 0.0338731\n",
      "[600]\ttrain's auc: 0.996519\ttrain's binary_logloss: 0.0503812\ttrain's binary_error: 0.019332\tvalid's auc: 0.946787\tvalid's binary_logloss: 0.0989813\tvalid's binary_error: 0.0329285\n",
      "[700]\ttrain's auc: 0.997849\ttrain's binary_logloss: 0.045201\ttrain's binary_error: 0.0169366\tvalid's auc: 0.947919\tvalid's binary_logloss: 0.0983206\tvalid's binary_error: 0.0334683\n",
      "[800]\ttrain's auc: 0.998668\ttrain's binary_logloss: 0.0408325\ttrain's binary_error: 0.014305\tvalid's auc: 0.948828\tvalid's binary_logloss: 0.098052\tvalid's binary_error: 0.0330634\n",
      "[900]\ttrain's auc: 0.999205\ttrain's binary_logloss: 0.0369227\ttrain's binary_error: 0.0120445\tvalid's auc: 0.949188\tvalid's binary_logloss: 0.0980148\tvalid's binary_error: 0.0327935\n",
      "[1000]\ttrain's auc: 0.999549\ttrain's binary_logloss: 0.0334039\ttrain's binary_error: 0.00958165\tvalid's auc: 0.950063\tvalid's binary_logloss: 0.0978761\tvalid's binary_error: 0.0331984\n",
      "Did not meet early stopping. Best iteration is:\n",
      "[1000]\ttrain's auc: 0.999549\ttrain's binary_logloss: 0.0334039\ttrain's binary_error: 0.00958165\tvalid's auc: 0.950063\tvalid's binary_logloss: 0.0978761\tvalid's binary_error: 0.0331984\n",
      "dict_keys(['train', 'valid'])\n",
      "3\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training until validation scores don't improve for 800 rounds\n",
      "[100]\ttrain's auc: 0.955424\ttrain's binary_logloss: 0.10438\ttrain's binary_error: 0.0340081\tvalid's auc: 0.924622\tvalid's binary_logloss: 0.118743\tvalid's binary_error: 0.0385965\n",
      "[200]\ttrain's auc: 0.974454\ttrain's binary_logloss: 0.0842026\ttrain's binary_error: 0.0296559\tvalid's auc: 0.935458\tvalid's binary_logloss: 0.109101\tvalid's binary_error: 0.0358974\n",
      "[300]\ttrain's auc: 0.984827\ttrain's binary_logloss: 0.07187\ttrain's binary_error: 0.026552\tvalid's auc: 0.939367\tvalid's binary_logloss: 0.10616\tvalid's binary_error: 0.0345479\n",
      "[400]\ttrain's auc: 0.991304\ttrain's binary_logloss: 0.0625198\ttrain's binary_error: 0.0244602\tvalid's auc: 0.941355\tvalid's binary_logloss: 0.104797\tvalid's binary_error: 0.0346829\n",
      "[500]\ttrain's auc: 0.994514\ttrain's binary_logloss: 0.0553073\ttrain's binary_error: 0.0214575\tvalid's auc: 0.942431\tvalid's binary_logloss: 0.104214\tvalid's binary_error: 0.0350877\n",
      "[600]\ttrain's auc: 0.996647\ttrain's binary_logloss: 0.0492428\ttrain's binary_error: 0.0190621\tvalid's auc: 0.943634\tvalid's binary_logloss: 0.103822\tvalid's binary_error: 0.0350877\n",
      "[700]\ttrain's auc: 0.997993\ttrain's binary_logloss: 0.0439752\ttrain's binary_error: 0.0159244\tvalid's auc: 0.944146\tvalid's binary_logloss: 0.103598\tvalid's binary_error: 0.0349528\n",
      "[800]\ttrain's auc: 0.998838\ttrain's binary_logloss: 0.0394629\ttrain's binary_error: 0.013664\tvalid's auc: 0.944501\tvalid's binary_logloss: 0.103361\tvalid's binary_error: 0.034413\n",
      "[900]\ttrain's auc: 0.99934\ttrain's binary_logloss: 0.0356271\ttrain's binary_error: 0.0109987\tvalid's auc: 0.944582\tvalid's binary_logloss: 0.103527\tvalid's binary_error: 0.034413\n",
      "[1000]\ttrain's auc: 0.99964\ttrain's binary_logloss: 0.0322808\ttrain's binary_error: 0.00846829\tvalid's auc: 0.944839\tvalid's binary_logloss: 0.10393\tvalid's binary_error: 0.0346829\n",
      "Did not meet early stopping. Best iteration is:\n",
      "[1000]\ttrain's auc: 0.99964\ttrain's binary_logloss: 0.0322808\ttrain's binary_error: 0.00846829\tvalid's auc: 0.944839\tvalid's binary_logloss: 0.10393\tvalid's binary_error: 0.0346829\n",
      "dict_keys(['train', 'valid'])\n",
      "3\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "32169"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import lightgbm as lgb\n",
    "import matplotlib.pyplot as plt\n",
    "seeds = [5, 4, 2, 209, 4096, 2048, 1024, 2015, 1015, 820]\n",
    "#seeds = [1993,2008,4096,1015]\n",
    "num_model_seed =1 #1,#4\n",
    "oof = np.zeros(X_train.shape[0])\n",
    "prediction = np.zeros(X_test.shape[0])\n",
    "pred = []\n",
    "prediction_each = []\n",
    "feat_imp_df = pd.DataFrame({'feats': feature_name, 'imp': 0})\n",
    "\n",
    "parameters = {\n",
    "    'learning_rate': 0.02,  #0.03：0.6155,0.02:0.6153\n",
    "    'max_depth': -1,#-1\n",
    "    'boosting_type': 'gbdt',\n",
    "    'objective': 'binary',\n",
    "    'metric': ['auc','binary_logloss','binary_error'],\n",
    "    'num_leaves': 12, #'num_leaves': 15, \n",
    "    'feature_fraction': 0.8,\n",
    "    'bagging_fraction': 0.8,\n",
    "    'bagging_freq': 5,\n",
    "    'seed': 2021,\n",
    "    # 'lambda_l2': 1,\n",
    "    'bagging_seed': 1,\n",
    "    'feature_fraction_seed': 7,\n",
    "    'min_data_in_leaf': 20,  #\n",
    "    'verbose': -1, \n",
    "    'n_jobs':8\n",
    "}\n",
    "fold = 5\n",
    "for model_seed in range(num_model_seed):\n",
    "    print(seeds[model_seed],\"--------------------------------------------------------------------------------------------\")\n",
    "    oof_cat = np.zeros(X_train.shape[0])\n",
    "    prediction_cat = np.zeros(X_test.shape[0])\n",
    "    skf = StratifiedKFold(n_splits=fold, random_state=seeds[model_seed], shuffle=True)\n",
    "    for index, (train_index, test_index) in enumerate(skf.split(X_train, y)):\n",
    "        train_x, test_x, train_y, test_y = X_train[feature_name].iloc[train_index], X_train[feature_name].iloc[test_index], y.iloc[train_index], y.iloc[test_index]\n",
    "        dtrain = lgb.Dataset(train_x, label=train_y)\n",
    "        dval = lgb.Dataset(test_x, label=test_y)\n",
    "        \n",
    "        lgb_evals={}\n",
    "        lgb_model = lgb.train(\n",
    "            parameters,\n",
    "            dtrain,\n",
    "            num_boost_round=1000,\n",
    "            valid_sets=[dtrain,dval],\n",
    "            valid_names=[\"train\",\"valid\"],\n",
    "            early_stopping_rounds=800, #early_stopping_rounds=200,early_stopping_rounds=500，early_stopping_rounds=150，#165\n",
    "            verbose_eval=100,\n",
    "            callbacks=[\n",
    "                lgb.record_evaluation(lgb_evals)\n",
    "            ]\n",
    "        )\n",
    "        oof_cat[test_index] += lgb_model.predict(test_x,num_iteration=lgb_model.best_iteration)\n",
    "        pred_each = lgb_model.predict(X_test,num_iteration=lgb_model.best_iteration)\n",
    "        prediction_each.append(pred_each)\n",
    "        prediction_cat += lgb_model.predict(X_test,num_iteration=lgb_model.best_iteration) / fold\n",
    "        feat_imp_df['imp'] += lgb_model.feature_importance() / fold\n",
    "        \n",
    "        print(lgb_evals.keys())\n",
    "        evals_result={'train':{},'test':{}}\n",
    "        evals_result['train']['auc'] = lgb_evals['train']['auc']\n",
    "        evals_result['test']['auc'] = lgb_evals['valid']['auc']\n",
    "        evals_result['train']['binary_error'] = lgb_evals['train']['binary_error']\n",
    "        evals_result['test']['binary_error'] = lgb_evals['valid']['binary_error']\n",
    "        evals_result['train']['logloss'] = lgb_evals['train']['binary_logloss']\n",
    "        evals_result['test']['logloss'] = lgb_evals['valid']['binary_logloss']\n",
    "        \n",
    "        plt.figure(figsize=(20, 5))\n",
    "        print(len(evals_result['train'].keys()))\n",
    "        i=1\n",
    "        for k in evals_result['train'].keys():\n",
    "            ax = plt.subplot(1,len(evals_result['train'].keys()),i)\n",
    "            ax.plot(evals_result['train'][k], label='train')\n",
    "            ax.plot(evals_result['test'][k], label='test')\n",
    "            # show the legend\n",
    "            ax.legend()\n",
    "            plt.title(k)\n",
    "            i +=1\n",
    "        plt.show()\n",
    "        \n",
    "        #print(\"f2_score:\",f_score(test_y,oof_cat[test_index]))\n",
    "        del train_x\n",
    "        del test_x\n",
    "        del train_y\n",
    "        del test_y\n",
    "        \n",
    "        # del lgb_model\n",
    "    oof += oof_cat / num_model_seed\n",
    "    prediction += prediction_cat / num_model_seed\n",
    "    pred.append(prediction_cat)\n",
    "gc.collect()\n",
    "\n",
    "### null importance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 6 特征重要性"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-07T06:50:13.476215Z",
     "iopub.status.busy": "2023-11-07T06:50:13.476018Z",
     "iopub.status.idle": "2023-11-07T06:50:13.490197Z",
     "shell.execute_reply": "2023-11-07T06:50:13.489692Z",
     "shell.execute_reply.started": "2023-11-07T06:50:13.476191Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>feats</th>\n",
       "      <th>imp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>668</th>\n",
       "      <td>TFT_CSTNOQRYTRNFLWmonth_mean</td>\n",
       "      <td>86.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>442</th>\n",
       "      <td>APSDCPTPRDNO_nunique_aps_qz_88851f9e0e9b482eac...</td>\n",
       "      <td>82.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>278</th>\n",
       "      <td>APSDTRAMT_ABS_min_aps_qz_88851f9e0e9b482eacf54...</td>\n",
       "      <td>57.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>QRYTRNFLW_times_std</td>\n",
       "      <td>56.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>798</th>\n",
       "      <td>APSDPRDNO_date_months_to_now_APSDTRAMT_kurt_0_net</td>\n",
       "      <td>51.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>615</th>\n",
       "      <td>TFT_TRNAMT_min_mbank_trn_mamt_3_790437b5c04ac6...</td>\n",
       "      <td>49.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>trnflw_custno_time_diff_min</td>\n",
       "      <td>48.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>161</th>\n",
       "      <td>DAY_AUM_BAL</td>\n",
       "      <td>48.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>time_diff_std_trnflw_last14</td>\n",
       "      <td>47.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>688</th>\n",
       "      <td>TFT_STDBSNCOD_countvec_4_y</td>\n",
       "      <td>46.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>865</th>\n",
       "      <td>mbank_trnflw_TFT_TRNAMT_data0515_0615sum</td>\n",
       "      <td>44.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>233</th>\n",
       "      <td>CARD_NO_APSDTRCHL_w2v_2</td>\n",
       "      <td>44.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>851</th>\n",
       "      <td>aps_APSDPRDNO_APSDTRCHL_strange_amts_sum_e0457...</td>\n",
       "      <td>44.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>648</th>\n",
       "      <td>CUST_NO_count_mbank_qry_mcnt_3_4a50500443ba497...</td>\n",
       "      <td>40.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>258</th>\n",
       "      <td>APSDTRAMT_min_aps_qz_3c6c4e45e982dc80246b40db4...</td>\n",
       "      <td>40.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>APSDCPTPRDNO_tfidf_1</td>\n",
       "      <td>39.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>QRYTRNFLW_cod_TFT_STDBSNCOD_nunique</td>\n",
       "      <td>38.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>660</th>\n",
       "      <td>DPFAPROD_SUM_LR</td>\n",
       "      <td>38.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>649</th>\n",
       "      <td>CUST_NO_count_mbank_qry_mcnt_3_6f6af3fc3864671...</td>\n",
       "      <td>38.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>498</th>\n",
       "      <td>daygap_std_mbank_trn</td>\n",
       "      <td>36.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>669</th>\n",
       "      <td>TFT_CSTNOQRYTRNFLWmonth_std</td>\n",
       "      <td>36.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>673</th>\n",
       "      <td>TFT_CSTNOQRYTRNFLWhour_min</td>\n",
       "      <td>35.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>817</th>\n",
       "      <td>aps_APSDPRDNO_date_months_to_now_strange_cpts_...</td>\n",
       "      <td>35.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>223</th>\n",
       "      <td>CARD_NO_CARD_NO_APSDCPTPRDNO_w2v_3</td>\n",
       "      <td>35.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>681</th>\n",
       "      <td>TFT_STDBSNCOD_tfidf_3_y</td>\n",
       "      <td>35.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>781</th>\n",
       "      <td>APSDPRDNO_APSDTRCHL_APSDTRAMT_max_e0457a5f5527...</td>\n",
       "      <td>35.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>870</th>\n",
       "      <td>mbank_qrytrnflw_TFT_STDBSNCOD_data0515_0615nun...</td>\n",
       "      <td>34.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>684</th>\n",
       "      <td>TFT_STDBSNCOD_tfidf_9_y</td>\n",
       "      <td>33.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>173</th>\n",
       "      <td>NTRL_CUST_AGE</td>\n",
       "      <td>33.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>245</th>\n",
       "      <td>hour_skew_aps_qz</td>\n",
       "      <td>32.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>165</th>\n",
       "      <td>DPSA_BAL</td>\n",
       "      <td>32.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>trnflw_custno_time_diff_std</td>\n",
       "      <td>31.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>QRYTRNFLW_times_mean</td>\n",
       "      <td>30.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>403</th>\n",
       "      <td>hour_std_aps_qz_0e30a5a85d29c720619212ed0721f096</td>\n",
       "      <td>30.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>655</th>\n",
       "      <td>CUST_NO_count_mbank_qry_wcnt_1_db0a93901176d46...</td>\n",
       "      <td>30.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>228</th>\n",
       "      <td>CARD_NO_APSDTRCOD_w2v_0</td>\n",
       "      <td>30.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>632</th>\n",
       "      <td>TFT_TRNAMT_std_mbank_trn_wamt_3_790437b5c04ac6...</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>455</th>\n",
       "      <td>CARD_NO_count_day_hour_std_2</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>716</th>\n",
       "      <td>APSDPRDNO_date_weeks_to_now_APSDTRAMT_std_8_in</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>687</th>\n",
       "      <td>TFT_STDBSNCOD_countvec_2_y</td>\n",
       "      <td>29.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>523</th>\n",
       "      <td>TFT_STDBSNCOD_count_mbank_qry_4a50500443ba4972...</td>\n",
       "      <td>29.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>773</th>\n",
       "      <td>APSDPRDNO_date_weeks_to_now_APSDTRAMT_kurt_8_out</td>\n",
       "      <td>29.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>APS_outamt_APSDTRAMT_std</td>\n",
       "      <td>29.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>431</th>\n",
       "      <td>daygap_min_aps_qz_8c9c388064b85b10604063ea2f01...</td>\n",
       "      <td>28.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>683</th>\n",
       "      <td>TFT_STDBSNCOD_tfidf_7_y</td>\n",
       "      <td>28.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>680</th>\n",
       "      <td>TFT_STDBSNCOD_tfidf_2_y</td>\n",
       "      <td>28.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>268</th>\n",
       "      <td>APSDTRAMT_std_aps_qz_3cc8c0101d1d765139bd9b79d...</td>\n",
       "      <td>28.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>756</th>\n",
       "      <td>APSDPRDNO_date_weeks_to_now_APSDTRAMT_max_1_out</td>\n",
       "      <td>28.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>337</th>\n",
       "      <td>dayofweek_mean_aps_qz_88851f9e0e9b482eacf54d9c...</td>\n",
       "      <td>27.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>344</th>\n",
       "      <td>dayofweek_skew_aps_qz_3c6c4e45e982dc80246b40db...</td>\n",
       "      <td>27.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 feats   imp\n",
       "668                       TFT_CSTNOQRYTRNFLWmonth_mean  86.2\n",
       "442  APSDCPTPRDNO_nunique_aps_qz_88851f9e0e9b482eac...  82.4\n",
       "278  APSDTRAMT_ABS_min_aps_qz_88851f9e0e9b482eacf54...  57.6\n",
       "74                                 QRYTRNFLW_times_std  56.2\n",
       "798  APSDPRDNO_date_months_to_now_APSDTRAMT_kurt_0_net  51.8\n",
       "615  TFT_TRNAMT_min_mbank_trn_mamt_3_790437b5c04ac6...  49.8\n",
       "7                          trnflw_custno_time_diff_min  48.2\n",
       "161                                        DAY_AUM_BAL  48.0\n",
       "45                         time_diff_std_trnflw_last14  47.0\n",
       "688                         TFT_STDBSNCOD_countvec_4_y  46.8\n",
       "865           mbank_trnflw_TFT_TRNAMT_data0515_0615sum  44.8\n",
       "233                            CARD_NO_APSDTRCHL_w2v_2  44.8\n",
       "851  aps_APSDPRDNO_APSDTRCHL_strange_amts_sum_e0457...  44.2\n",
       "648  CUST_NO_count_mbank_qry_mcnt_3_4a50500443ba497...  40.6\n",
       "258  APSDTRAMT_min_aps_qz_3c6c4e45e982dc80246b40db4...  40.4\n",
       "199                               APSDCPTPRDNO_tfidf_1  39.2\n",
       "85                 QRYTRNFLW_cod_TFT_STDBSNCOD_nunique  38.8\n",
       "660                                    DPFAPROD_SUM_LR  38.6\n",
       "649  CUST_NO_count_mbank_qry_mcnt_3_6f6af3fc3864671...  38.4\n",
       "498                               daygap_std_mbank_trn  36.8\n",
       "669                        TFT_CSTNOQRYTRNFLWmonth_std  36.6\n",
       "673                         TFT_CSTNOQRYTRNFLWhour_min  35.8\n",
       "817  aps_APSDPRDNO_date_months_to_now_strange_cpts_...  35.8\n",
       "223                 CARD_NO_CARD_NO_APSDCPTPRDNO_w2v_3  35.4\n",
       "681                            TFT_STDBSNCOD_tfidf_3_y  35.2\n",
       "781  APSDPRDNO_APSDTRCHL_APSDTRAMT_max_e0457a5f5527...  35.2\n",
       "870  mbank_qrytrnflw_TFT_STDBSNCOD_data0515_0615nun...  34.6\n",
       "684                            TFT_STDBSNCOD_tfidf_9_y  33.4\n",
       "173                                      NTRL_CUST_AGE  33.2\n",
       "245                                   hour_skew_aps_qz  32.8\n",
       "165                                           DPSA_BAL  32.0\n",
       "9                          trnflw_custno_time_diff_std  31.0\n",
       "71                                QRYTRNFLW_times_mean  30.6\n",
       "403   hour_std_aps_qz_0e30a5a85d29c720619212ed0721f096  30.4\n",
       "655  CUST_NO_count_mbank_qry_wcnt_1_db0a93901176d46...  30.2\n",
       "228                            CARD_NO_APSDTRCOD_w2v_0  30.2\n",
       "632  TFT_TRNAMT_std_mbank_trn_wamt_3_790437b5c04ac6...  30.0\n",
       "455                       CARD_NO_count_day_hour_std_2  30.0\n",
       "716     APSDPRDNO_date_weeks_to_now_APSDTRAMT_std_8_in  30.0\n",
       "687                         TFT_STDBSNCOD_countvec_2_y  29.8\n",
       "523  TFT_STDBSNCOD_count_mbank_qry_4a50500443ba4972...  29.6\n",
       "773   APSDPRDNO_date_weeks_to_now_APSDTRAMT_kurt_8_out  29.4\n",
       "116                           APS_outamt_APSDTRAMT_std  29.2\n",
       "431  daygap_min_aps_qz_8c9c388064b85b10604063ea2f01...  28.6\n",
       "683                            TFT_STDBSNCOD_tfidf_7_y  28.4\n",
       "680                            TFT_STDBSNCOD_tfidf_2_y  28.4\n",
       "268  APSDTRAMT_std_aps_qz_3cc8c0101d1d765139bd9b79d...  28.2\n",
       "756    APSDPRDNO_date_weeks_to_now_APSDTRAMT_max_1_out  28.2\n",
       "337  dayofweek_mean_aps_qz_88851f9e0e9b482eacf54d9c...  27.8\n",
       "344  dayofweek_skew_aps_qz_3c6c4e45e982dc80246b40db...  27.8"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feat_imp_df.sort_values(by=['imp'],ascending=False, inplace=True)\n",
    "feat_imp_df.head(50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-07T06:50:13.491198Z",
     "iopub.status.busy": "2023-11-07T06:50:13.491009Z",
     "iopub.status.idle": "2023-11-07T06:50:14.166094Z",
     "shell.execute_reply": "2023-11-07T06:50:14.165545Z",
     "shell.execute_reply.started": "2023-11-07T06:50:13.491177Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.figure(figsize = [20,20])\n",
    "sns.barplot(x = feat_imp_df.iloc[:50]['imp'], y = feat_imp_df.iloc[:50]['feats'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 7 线下指标"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-07T06:50:14.167174Z",
     "iopub.status.busy": "2023-11-07T06:50:14.166973Z",
     "iopub.status.idle": "2023-11-07T06:50:14.173112Z",
     "shell.execute_reply": "2023-11-07T06:50:14.172575Z",
     "shell.execute_reply.started": "2023-11-07T06:50:14.167150Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report,precision_score,recall_score,f1_score,precision_recall_curve\n",
    "from sklearn.metrics import roc_curve, auc\n",
    "def f_score(y_true, y_pred):\n",
    "\n",
    "    precisions, recalls, thresholds = precision_recall_curve(y_true, y_pred)\n",
    "\n",
    "    # F1\n",
    "#     f1_scores = (2 * precisions * recalls) / (precisions + recalls)\n",
    "#     best_t = thresholds[np.argmax(f1_scores[np.isfinite(f1_scores)])]\n",
    "#     y_1 = [1 if x >= best_t else 0 for x in y_pred]\n",
    "#     recall = recall_score(y_true, y_1)\n",
    "#     precision = precision_score(y_true, y_1)\n",
    "#     F_score = f1_score(y_true, y_1)\n",
    "#     F_score = (2 * precision * recall) / (precision + recall)\n",
    "\n",
    "    # F2\n",
    "    #f2_scores = (5 * precisions * recalls) / (4*precisions + recalls)\n",
    "    #best_t = thresholds[np.argmax(f2_scores[np.isfinite(f2_scores)])]\n",
    "    #y_1 = [1 if x >= best_t else 0 for x in y_pred]\n",
    "    recall = recall_score(y_true, y_pred)\n",
    "    precision = precision_score(y_true, y_pred)\n",
    "    F_score = (5 * precision * recall) / (4*precision + recall)\n",
    "\n",
    "    #print(f\"valid's f1: {F_score}\")\n",
    "    #print(\"最佳阈值: \", str(best_t))\n",
    "    #print('打印分类报告')\n",
    "    #clf_report1 = classification_report(y_true.values, y_1)\n",
    "    #print(clf_report1)\n",
    "    return F_score\n",
    "\n",
    "def ks(y_true, y_pred):\n",
    "    fpr, tpr ,th = roc_curve(y_true, y_pred)\n",
    "#     ks_metrics = max(tpr-fpr)\n",
    "    auc_metrics = auc(fpr, tpr)\n",
    "    #print(f\"valid's ks: {ks_metrics}\")\n",
    "    print(f\"valid's auc: {auc_metrics}\")\n",
    "    return  auc_metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-07T06:50:14.174076Z",
     "iopub.status.busy": "2023-11-07T06:50:14.173898Z",
     "iopub.status.idle": "2023-11-07T06:50:14.188866Z",
     "shell.execute_reply": "2023-11-07T06:50:14.188356Z",
     "shell.execute_reply.started": "2023-11-07T06:50:14.174054Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "valid's auc: 0.9449974870333846\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.9449974870333846"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ks(y,oof)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 8 未固定1的个数搜索阈值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-07T06:50:14.189712Z",
     "iopub.status.busy": "2023-11-07T06:50:14.189545Z",
     "iopub.status.idle": "2023-11-07T06:50:18.533890Z",
     "shell.execute_reply": "2023-11-07T06:50:18.533199Z",
     "shell.execute_reply.started": "2023-11-07T06:50:14.189691Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 10%|▉         | 49/501 [00:04<00:40, 11.30it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6643853879655142 0.078\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "# 搜索线下最佳阈值\n",
    "t0, v = 0.05, 0.001\n",
    "best_t, best_score = t0, 0\n",
    "best_step, early_stop = 0, 20\n",
    "for step in tqdm(range(501)):\n",
    "    curr_t = t0 + step * v\n",
    "    y_ = [1 if x >= curr_t else 0 for x in oof]\n",
    "    curr_score = f_score(y.values, y_)\n",
    "    if curr_score > best_score:\n",
    "        best_t = curr_t\n",
    "        best_score = curr_score\n",
    "        best_step = step\n",
    "    elif step - best_step > early_stop:\n",
    "        break\n",
    "print(best_score, best_t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-07T06:50:18.534884Z",
     "iopub.status.busy": "2023-11-07T06:50:18.534693Z",
     "iopub.status.idle": "2023-11-07T06:50:18.636547Z",
     "shell.execute_reply": "2023-11-07T06:50:18.635915Z",
     "shell.execute_reply.started": "2023-11-07T06:50:18.534860Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.99      0.94      0.96     35100\n",
      "         1.0       0.43      0.77      0.55      1950\n",
      "\n",
      "    accuracy                           0.93     37050\n",
      "   macro avg       0.71      0.86      0.76     37050\n",
      "weighted avg       0.96      0.93      0.94     37050\n",
      "\n"
     ]
    }
   ],
   "source": [
    "y_1 = [1 if x >= best_t else 0 for x in oof]\n",
    "# 分类报告\n",
    "from sklearn.metrics import classification_report\n",
    "clf_report1 = classification_report(y.values, y_1)\n",
    "print(clf_report1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 9 固定1的个数搜索阈值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-07T06:50:18.637512Z",
     "iopub.status.busy": "2023-11-07T06:50:18.637333Z",
     "iopub.status.idle": "2023-11-07T06:50:18.641737Z",
     "shell.execute_reply": "2023-11-07T06:50:18.641239Z",
     "shell.execute_reply.started": "2023-11-07T06:50:18.637490Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\n# 1029\\n\\n# 1029 固定1的个数\\nfrom sklearn.metrics import classification_report, precision_score, recall_score, f1_score, precision_recall_curve,  roc_curve, auc\\n\\ndef do_metrics(y_true, y_pred, y_pred_test, count_1=427):\\n    # 寻找最佳阈值\\n    t0, v = 0.05, 0.0001\\n    best_t, best_score = t0, 0\\n    best_step = 0\\n    for step in tqdm(range(1001)):\\n        curr_t = t0 + step * v\\n        y_ = [1 if x >= curr_t else 0 for x in oof]\\n        y_test_ = [1 if x >= curr_t else 0 for x in y_pred_test] \\n        curr_score = f_score(y.values, y_)\\n        if curr_score > best_score and sum(y_test_) == count_1:\\n            best_t = curr_t\\n            best_score = curr_score\\n            best_step = step\\n            print(\"训练集F2: {}, 阈值: {}\".format(best_score, best_t))\\n\\n    print(\"最佳训练集F2: {}, 最佳阈值: {}\".format(best_score, best_t))\\n\\n    # 打印分类报表\\n    y_1 = [1 if x >= best_t else 0 for x in y_pred]\\n    clf_report1 = classification_report(y_true.values, y_1)\\n\\n    curr_f1score = f_score(y_true, y_1)\\n    recall = recall_score(y_true, y_1)\\n    precision = precision_score(y_true, y_1)\\n\\n    fpr, tpr, th = roc_curve(y, oof)\\n\\n    print(f\"train\\'s precision: {precision}\")\\n    print(f\"train\\'s recall: {recall}\")\\n\\n    print(f\"train\\'s auc: {auc(fpr,tpr)}\")\\n    print(f\"train\\'s ks: {max(tpr-fpr)}\")\\n    print(f\"train\\'s F2: {curr_f1score}\")\\n    print(f\"最佳阈值:  {str(best_t)}\")\\n    print(\\'打印分类报告: \\')\\n    print(clf_report1)\\n\\n    return best_t, best_score\\n\\nbest_t, curr_f1score = do_metrics(y, oof, prediction) #(prediction_each[2]+prediction_each[4])/2, prediction\\n'"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''\n",
    "# 1029\n",
    "\n",
    "# 1029 固定1的个数\n",
    "from sklearn.metrics import classification_report, precision_score, recall_score, f1_score, precision_recall_curve,  roc_curve, auc\n",
    "\n",
    "def do_metrics(y_true, y_pred, y_pred_test, count_1=427):\n",
    "    # 寻找最佳阈值\n",
    "    t0, v = 0.05, 0.0001\n",
    "    best_t, best_score = t0, 0\n",
    "    best_step = 0\n",
    "    for step in tqdm(range(1001)):\n",
    "        curr_t = t0 + step * v\n",
    "        y_ = [1 if x >= curr_t else 0 for x in oof]\n",
    "        y_test_ = [1 if x >= curr_t else 0 for x in y_pred_test] \n",
    "        curr_score = f_score(y.values, y_)\n",
    "        if curr_score > best_score and sum(y_test_) == count_1:\n",
    "            best_t = curr_t\n",
    "            best_score = curr_score\n",
    "            best_step = step\n",
    "            print(\"训练集F2: {}, 阈值: {}\".format(best_score, best_t))\n",
    "\n",
    "    print(\"最佳训练集F2: {}, 最佳阈值: {}\".format(best_score, best_t))\n",
    "\n",
    "    # 打印分类报表\n",
    "    y_1 = [1 if x >= best_t else 0 for x in y_pred]\n",
    "    clf_report1 = classification_report(y_true.values, y_1)\n",
    "\n",
    "    curr_f1score = f_score(y_true, y_1)\n",
    "    recall = recall_score(y_true, y_1)\n",
    "    precision = precision_score(y_true, y_1)\n",
    "\n",
    "    fpr, tpr, th = roc_curve(y, oof)\n",
    "\n",
    "    print(f\"train's precision: {precision}\")\n",
    "    print(f\"train's recall: {recall}\")\n",
    "\n",
    "    print(f\"train's auc: {auc(fpr,tpr)}\")\n",
    "    print(f\"train's ks: {max(tpr-fpr)}\")\n",
    "    print(f\"train's F2: {curr_f1score}\")\n",
    "    print(f\"最佳阈值:  {str(best_t)}\")\n",
    "    print('打印分类报告: ')\n",
    "    print(clf_report1)\n",
    "\n",
    "    return best_t, best_score\n",
    "\n",
    "best_t, curr_f1score = do_metrics(y, oof, prediction) #(prediction_each[2]+prediction_each[4])/2, prediction\n",
    "'''"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 10 保存结果"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-07T06:50:18.642648Z",
     "iopub.status.busy": "2023-11-07T06:50:18.642486Z",
     "iopub.status.idle": "2023-11-07T06:50:18.952164Z",
     "shell.execute_reply": "2023-11-07T06:50:18.951519Z",
     "shell.execute_reply.started": "2023-11-07T06:50:18.642628Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1的个数 480\n",
      "1的个数比例 0.09432108469247397\n",
      "20231107\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-97-44e261d61006>:7: FutureWarning: The pandas.datetime class is deprecated and will be removed from pandas in a future version. Import from datetime module instead.\n",
      "  data_date=pd.datetime.now().strftime('%Y%m%d')\n"
     ]
    }
   ],
   "source": [
    "# 保存测试集预测结果\n",
    "test2 = df_test.copy()\n",
    "test2['FLAG'] = [1 if x >= best_t else 0 for x in prediction]  \n",
    "count_1 = test2[test2['FLAG']>= best_t] ['FLAG'].count() ##best_t\n",
    "print('1的个数', count_1)\n",
    "print ('1的个数比例', count_1/5089)\n",
    "data_date=pd.datetime.now().strftime('%Y%m%d')\n",
    "print(data_date)\n",
    "#test2[['CUST_NO', 'FLAG']].to_csv('../yxh/lgb_455_B1107.csv', header=None, index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 11 保存概率值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-07T06:50:18.952973Z",
     "iopub.status.busy": "2023-11-07T06:50:18.952804Z",
     "iopub.status.idle": "2023-11-07T06:50:19.712731Z",
     "shell.execute_reply": "2023-11-07T06:50:19.712115Z",
     "shell.execute_reply.started": "2023-11-07T06:50:18.952952Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#保存预测结果概率\n",
    "test_pre = df_test.copy()\n",
    "test_pre['pre']= prediction\n",
    "test_pre[['CUST_NO','pre']].to_csv('./tmp/B_lgb_test_prediction.csv',index=False,header=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-07T06:50:19.713880Z",
     "iopub.status.busy": "2023-11-07T06:50:19.713691Z",
     "iopub.status.idle": "2023-11-07T06:50:19.871359Z",
     "shell.execute_reply": "2023-11-07T06:50:19.870645Z",
     "shell.execute_reply.started": "2023-11-07T06:50:19.713857Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# 保存oof概率\n",
    "df_oof=df_train[['CUST_NO']].copy()\n",
    "df_oof['oof']= oof\n",
    "df_oof[['CUST_NO','oof']].to_csv('./tmp/B_lgb_train_oof.csv',index=False,header=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
